diff --git a/TestModel_mod.idf b/TestModel_mod.idf new file mode 100644 index 0000000..90a76ee --- /dev/null +++ b/TestModel_mod.idf @@ -0,0 +1,8687 @@ +!- Windows Line endings + +Version, + 23.1; !- Version Identifier + +SimulationControl, + Yes, !- Do Zone Sizing Calculation + Yes, !- Do System Sizing Calculation + No, !- Do Plant Sizing Calculation + No, !- Run Simulation for Sizing Periods + Yes, !- Run Simulation for Weather File Run Periods + , !- Do HVAC Sizing Simulation for Sizing Periods + ; !- Maximum Number of HVAC Sizing Simulation Passes + +Building, + Building, !- Name + 0, !- North Axis + Suburbs, !- Terrain + 0.04, !- Loads Convergence Tolerance Value + 0.4, !- Temperature Convergence Tolerance Value + FullExterior, !- Solar Distribution + 25, !- Maximum Number of Warmup Days + 6; !- Minimum Number of Warmup Days + +ShadowCalculation, + PolygonClipping, !- Shading Calculation Method + Periodic, !- Shading Calculation Update Frequency Method + 20, !- Shading Calculation Update Frequency + 15000, !- Maximum Figures in Shadow Overlap Calculations + SutherlandHodgman, !- Polygon Clipping Algorithm + , !- Pixel Counting Resolution + SimpleSkyDiffuseModeling; !- Sky Diffuse Modeling Algorithm + +SurfaceConvectionAlgorithm:Inside, + TARP; !- Algorithm + +SurfaceConvectionAlgorithm:Outside, + DOE-2; !- Algorithm + +HeatBalanceAlgorithm, + ConductionTransferFunction, !- Algorithm + 2000, !- Surface Temperature Upper Limit + 0.00000001, !- Minimum Surface Convection Heat Transfer Coefficient Value + 1000; !- Maximum Surface Convection Heat Transfer Coefficient Value + +ZoneCapacitanceMultiplier:ResearchSpecial, + All, !- Name + , !- Zone or ZoneList Name + 1, !- Temperature Capacity Multiplier + 1, !- Humidity Capacity Multiplier + 1, !- Carbon Dioxide Capacity Multiplier + 1; !- Generic Contaminant Capacity Multiplier + +Timestep, + 6; !- Number of Timesteps per Hour + +ConvergenceLimits, + 1, !- Minimum System Timestep + 25; !- Maximum HVAC Iterations + +Site:Location, + Untitled (01-01:31-12) (01-01:31-12), !- Name + 40.45, !- Latitude + -3.55, !- Longitude + 1, !- Time Zone + 582; !- Elevation + +SizingPeriod:DesignDay, + Summer Design Day in Untitled (01-01:31-12) Jul, !- Name + 7, !- Month + 15, !- Day of Month + SummerDesignDay, !- Day Type + 38.2, !- Maximum DryBulb Temperature + 17.5, !- Daily DryBulb Temperature Range + , !- DryBulb Temperature Range Modifier Type + , !- DryBulb Temperature Range Modifier Day Schedule Name + WetBulb, !- Humidity Condition Type + 20.3, !- Wetbulb or DewPoint at Maximum DryBulb + , !- Humidity Condition Day Schedule Name + , !- Humidity Ratio at Maximum DryBulb + , !- Enthalpy at Maximum DryBulb + , !- Daily WetBulb Temperature Range + 94525.9, !- Barometric Pressure + 0, !- Wind Speed + 0, !- Wind Direction + No, !- Rain Indicator + No, !- Snow Indicator + Yes, !- Daylight Saving Time Indicator + ASHRAEClearSky, !- Solar Model Indicator + , !- Beam Solar Day Schedule Name + , !- Diffuse Solar Day Schedule Name + , !- ASHRAE Clear Sky Optical Depth for Beam Irradiance taub + , !- ASHRAE Clear Sky Optical Depth for Diffuse Irradiance taud + 0.98; !- Sky Clearness + +SizingPeriod:DesignDay, + Summer Design Day in Untitled (01-01:31-12) Aug, !- Name + 8, !- Month + 15, !- Day of Month + SummerDesignDay, !- Day Type + 38, !- Maximum DryBulb Temperature + 17.4, !- Daily DryBulb Temperature Range + , !- DryBulb Temperature Range Modifier Type + , !- DryBulb Temperature Range Modifier Day Schedule Name + WetBulb, !- Humidity Condition Type + 19.9, !- Wetbulb or DewPoint at Maximum DryBulb + , !- Humidity Condition Day Schedule Name + , !- Humidity Ratio at Maximum DryBulb + , !- Enthalpy at Maximum DryBulb + , !- Daily WetBulb Temperature Range + 94525.9, !- Barometric Pressure + 0, !- Wind Speed + 0, !- Wind Direction + No, !- Rain Indicator + No, !- Snow Indicator + Yes, !- Daylight Saving Time Indicator + ASHRAEClearSky, !- Solar Model Indicator + , !- Beam Solar Day Schedule Name + , !- Diffuse Solar Day Schedule Name + , !- ASHRAE Clear Sky Optical Depth for Beam Irradiance taub + , !- ASHRAE Clear Sky Optical Depth for Diffuse Irradiance taud + 0.98; !- Sky Clearness + +SizingPeriod:DesignDay, + Summer Design Day in Untitled (01-01:31-12) Sep, !- Name + 9, !- Month + 15, !- Day of Month + SummerDesignDay, !- Day Type + 34.9, !- Maximum DryBulb Temperature + 16.8, !- Daily DryBulb Temperature Range + , !- DryBulb Temperature Range Modifier Type + , !- DryBulb Temperature Range Modifier Day Schedule Name + WetBulb, !- Humidity Condition Type + 19.3, !- Wetbulb or DewPoint at Maximum DryBulb + , !- Humidity Condition Day Schedule Name + , !- Humidity Ratio at Maximum DryBulb + , !- Enthalpy at Maximum DryBulb + , !- Daily WetBulb Temperature Range + 94525.9, !- Barometric Pressure + 0, !- Wind Speed + 0, !- Wind Direction + No, !- Rain Indicator + No, !- Snow Indicator + Yes, !- Daylight Saving Time Indicator + ASHRAEClearSky, !- Solar Model Indicator + , !- Beam Solar Day Schedule Name + , !- Diffuse Solar Day Schedule Name + , !- ASHRAE Clear Sky Optical Depth for Beam Irradiance taub + , !- ASHRAE Clear Sky Optical Depth for Diffuse Irradiance taud + 0.98; !- Sky Clearness + +SizingPeriod:DesignDay, + Winter Design Day in Untitled (01-01:31-12), !- Name + 1, !- Month + 15, !- Day of Month + WinterDesignDay, !- Day Type + -4, !- Maximum DryBulb Temperature + 0, !- Daily DryBulb Temperature Range + , !- DryBulb Temperature Range Modifier Type + , !- DryBulb Temperature Range Modifier Day Schedule Name + WetBulb, !- Humidity Condition Type + -4, !- Wetbulb or DewPoint at Maximum DryBulb + , !- Humidity Condition Day Schedule Name + , !- Humidity Ratio at Maximum DryBulb + , !- Enthalpy at Maximum DryBulb + , !- Daily WetBulb Temperature Range + 94525.9, !- Barometric Pressure + 11.6, !- Wind Speed + 0, !- Wind Direction + No, !- Rain Indicator + No, !- Snow Indicator + No, !- Daylight Saving Time Indicator + , !- Solar Model Indicator + , !- Beam Solar Day Schedule Name + , !- Diffuse Solar Day Schedule Name + , !- ASHRAE Clear Sky Optical Depth for Beam Irradiance taub + , !- ASHRAE Clear Sky Optical Depth for Diffuse Irradiance taud + 0; !- Sky Clearness + +RunPeriod, + Untitled (01-01:31-12), !- Name + 1, !- Begin Month + 1, !- Begin Day of Month + 2002, !- Begin Year + 12, !- End Month + 31, !- End Day of Month + 2002, !- End Year + , !- Day of Week for Start Day + No, !- Use Weather File Holidays and Special Days + No, !- Use Weather File Daylight Saving Period + Yes, !- Apply Weekend Holiday Rule + Yes, !- Use Weather File Rain Indicators + Yes, !- Use Weather File Snow Indicators + No; !- Treat Weather as Actual + +RunPeriodControl:DaylightSavingTime, + Last Sunday in March, !- Start Date + Last Sunday in October; !- End Date + +Site:GroundTemperature:BuildingSurface, + 18, !- January Ground Temperature + 18, !- February Ground Temperature + 18, !- March Ground Temperature + 18, !- April Ground Temperature + 18, !- May Ground Temperature + 18, !- June Ground Temperature + 18, !- July Ground Temperature + 18, !- August Ground Temperature + 18, !- September Ground Temperature + 18, !- October Ground Temperature + 18, !- November Ground Temperature + 18; !- December Ground Temperature + +Site:GroundTemperature:FCfactorMethod, + 6.09, !- January Ground Temperature + 5.28, !- February Ground Temperature + 6.71, !- March Ground Temperature + 8.95, !- April Ground Temperature + 14.81, !- May Ground Temperature + 19.34, !- June Ground Temperature + 22.39, !- July Ground Temperature + 23.32, !- August Ground Temperature + 21.74, !- September Ground Temperature + 18.22, !- October Ground Temperature + 13.53, !- November Ground Temperature + 9.18; !- December Ground Temperature + +Site:GroundTemperature:Shallow, + 14, !- January Surface Ground Temperature + 14, !- February Surface Ground Temperature + 14, !- March Surface Ground Temperature + 14, !- April Surface Ground Temperature + 14, !- May Surface Ground Temperature + 14, !- June Surface Ground Temperature + 14, !- July Surface Ground Temperature + 14, !- August Surface Ground Temperature + 14, !- September Surface Ground Temperature + 14, !- October Surface Ground Temperature + 14, !- November Surface Ground Temperature + 14; !- December Surface Ground Temperature + +Site:GroundTemperature:Deep, + 14, !- January Deep Ground Temperature + 14, !- February Deep Ground Temperature + 14, !- March Deep Ground Temperature + 14, !- April Deep Ground Temperature + 14, !- May Deep Ground Temperature + 14, !- June Deep Ground Temperature + 14, !- July Deep Ground Temperature + 14, !- August Deep Ground Temperature + 14, !- September Deep Ground Temperature + 14, !- October Deep Ground Temperature + 14, !- November Deep Ground Temperature + 14; !- December Deep Ground Temperature + +Site:GroundReflectance, + 0.2, !- January Ground Reflectance + 0.2, !- February Ground Reflectance + 0.2, !- March Ground Reflectance + 0.2, !- April Ground Reflectance + 0.2, !- May Ground Reflectance + 0.2, !- June Ground Reflectance + 0.2, !- July Ground Reflectance + 0.2, !- August Ground Reflectance + 0.2, !- September Ground Reflectance + 0.2, !- October Ground Reflectance + 0.2, !- November Ground Reflectance + 0.2; !- December Ground Reflectance + +Site:GroundReflectance:SnowModifier, + 2.0, !- Ground Reflected Solar Modifier + 2.0; !- Daylighting Ground Reflected Solar Modifier + +ScheduleTypeLimits, + Any Number; !- Name + +ScheduleTypeLimits, + Fraction, !- Name + 0.0, !- Lower Limit Value + 1.0, !- Upper Limit Value + CONTINUOUS; !- Numeric Type + +ScheduleTypeLimits, + Temperature, !- Name + -60, !- Lower Limit Value + 200, !- Upper Limit Value + CONTINUOUS; !- Numeric Type + +ScheduleTypeLimits, + Control Type, !- Name + 0, !- Lower Limit Value + 4, !- Upper Limit Value + DISCRETE; !- Numeric Type + +ScheduleTypeLimits, + On/Off, !- Name + 0, !- Lower Limit Value + 1, !- Upper Limit Value + DISCRETE; !- Numeric Type + +Schedule:Day:Hourly, + 0, !- Name + Any Number, !- Schedule Type Limits Name + 0, !- Hour 1 + 0, !- Hour 2 + 0, !- Hour 3 + 0, !- Hour 4 + 0, !- Hour 5 + 0, !- Hour 6 + 0, !- Hour 7 + 0, !- Hour 8 + 0, !- Hour 9 + 0, !- Hour 10 + 0, !- Hour 11 + 0, !- Hour 12 + 0, !- Hour 13 + 0, !- Hour 14 + 0, !- Hour 15 + 0, !- Hour 16 + 0, !- Hour 17 + 0, !- Hour 18 + 0, !- Hour 19 + 0, !- Hour 20 + 0, !- Hour 21 + 0, !- Hour 22 + 0, !- Hour 23 + 0; !- Hour 24 + +Schedule:Day:Hourly, + 50, !- Name + Any Number, !- Schedule Type Limits Name + 50, !- Hour 1 + 50, !- Hour 2 + 50, !- Hour 3 + 50, !- Hour 4 + 50, !- Hour 5 + 50, !- Hour 6 + 50, !- Hour 7 + 50, !- Hour 8 + 50, !- Hour 9 + 50, !- Hour 10 + 50, !- Hour 11 + 50, !- Hour 12 + 50, !- Hour 13 + 50, !- Hour 14 + 50, !- Hour 15 + 50, !- Hour 16 + 50, !- Hour 17 + 50, !- Hour 18 + 50, !- Hour 19 + 50, !- Hour 20 + 50, !- Hour 21 + 50, !- Hour 22 + 50, !- Hour 23 + 50; !- Hour 24 + +Schedule:Day:Hourly, + Off, !- Name + Fraction, !- Schedule Type Limits Name + 0, !- Hour 1 + 0, !- Hour 2 + 0, !- Hour 3 + 0, !- Hour 4 + 0, !- Hour 5 + 0, !- Hour 6 + 0, !- Hour 7 + 0, !- Hour 8 + 0, !- Hour 9 + 0, !- Hour 10 + 0, !- Hour 11 + 0, !- Hour 12 + 0, !- Hour 13 + 0, !- Hour 14 + 0, !- Hour 15 + 0, !- Hour 16 + 0, !- Hour 17 + 0, !- Hour 18 + 0, !- Hour 19 + 0, !- Hour 20 + 0, !- Hour 21 + 0, !- Hour 22 + 0, !- Hour 23 + 0; !- Hour 24 + +Schedule:Day:Hourly, + Summer Control Type Day Sch, !- Name + Control Type, !- Schedule Type Limits Name + 4, !- Hour 1 + 4, !- Hour 2 + 4, !- Hour 3 + 4, !- Hour 4 + 4, !- Hour 5 + 4, !- Hour 6 + 4, !- Hour 7 + 4, !- Hour 8 + 4, !- Hour 9 + 4, !- Hour 10 + 4, !- Hour 11 + 4, !- Hour 12 + 4, !- Hour 13 + 4, !- Hour 14 + 4, !- Hour 15 + 4, !- Hour 16 + 4, !- Hour 17 + 4, !- Hour 18 + 4, !- Hour 19 + 4, !- Hour 20 + 4, !- Hour 21 + 4, !- Hour 22 + 4, !- Hour 23 + 4; !- Hour 24 + +Schedule:Day:Hourly, + Winter Control Type Day Sch, !- Name + Control Type, !- Schedule Type Limits Name + 4, !- Hour 1 + 4, !- Hour 2 + 4, !- Hour 3 + 4, !- Hour 4 + 4, !- Hour 5 + 4, !- Hour 6 + 4, !- Hour 7 + 4, !- Hour 8 + 4, !- Hour 9 + 4, !- Hour 10 + 4, !- Hour 11 + 4, !- Hour 12 + 4, !- Hour 13 + 4, !- Hour 14 + 4, !- Hour 15 + 4, !- Hour 16 + 4, !- Hour 17 + 4, !- Hour 18 + 4, !- Hour 19 + 4, !- Hour 20 + 4, !- Hour 21 + 4, !- Hour 22 + 4, !- Hour 23 + 4; !- Hour 24 + +Schedule:Day:Hourly, + Summer Control Type Day Sch - cool, !- Name + Control Type, !- Schedule Type Limits Name + 2, !- Hour 1 + 2, !- Hour 2 + 2, !- Hour 3 + 2, !- Hour 4 + 2, !- Hour 5 + 2, !- Hour 6 + 2, !- Hour 7 + 2, !- Hour 8 + 2, !- Hour 9 + 2, !- Hour 10 + 2, !- Hour 11 + 2, !- Hour 12 + 2, !- Hour 13 + 2, !- Hour 14 + 2, !- Hour 15 + 2, !- Hour 16 + 2, !- Hour 17 + 2, !- Hour 18 + 2, !- Hour 19 + 2, !- Hour 20 + 2, !- Hour 21 + 2, !- Hour 22 + 2, !- Hour 23 + 2; !- Hour 24 + +Schedule:Day:Hourly, + Winter Control Type Day Sch - heat, !- Name + Control Type, !- Schedule Type Limits Name + 1, !- Hour 1 + 1, !- Hour 2 + 1, !- Hour 3 + 1, !- Hour 4 + 1, !- Hour 5 + 1, !- Hour 6 + 1, !- Hour 7 + 1, !- Hour 8 + 1, !- Hour 9 + 1, !- Hour 10 + 1, !- Hour 11 + 1, !- Hour 12 + 1, !- Hour 13 + 1, !- Hour 14 + 1, !- Hour 15 + 1, !- Hour 16 + 1, !- Hour 17 + 1, !- Hour 18 + 1, !- Hour 19 + 1, !- Hour 20 + 1, !- Hour 21 + 1, !- Hour 22 + 1, !- Hour 23 + 1; !- Hour 24 + +Schedule:Week:Daily, + Off, !- Name + Off, !- Sunday ScheduleDay Name + Off, !- Monday ScheduleDay Name + Off, !- Tuesday ScheduleDay Name + Off, !- Wednesday ScheduleDay Name + Off, !- Thursday ScheduleDay Name + Off, !- Friday ScheduleDay Name + Off, !- Saturday ScheduleDay Name + Off, !- Holiday ScheduleDay Name + Off, !- SummerDesignDay ScheduleDay Name + Off, !- WinterDesignDay ScheduleDay Name + Off, !- CustomDay1 ScheduleDay Name + Off; !- CustomDay2 ScheduleDay Name + +Schedule:Week:Daily, + Summer Control Type Week Sch, !- Name + Summer Control Type Day Sch, !- Sunday ScheduleDay Name + Summer Control Type Day Sch, !- Monday ScheduleDay Name + Summer Control Type Day Sch, !- Tuesday ScheduleDay Name + Summer Control Type Day Sch, !- Wednesday ScheduleDay Name + Summer Control Type Day Sch, !- Thursday ScheduleDay Name + Summer Control Type Day Sch, !- Friday ScheduleDay Name + Summer Control Type Day Sch, !- Saturday ScheduleDay Name + Summer Control Type Day Sch, !- Holiday ScheduleDay Name + Summer Control Type Day Sch, !- SummerDesignDay ScheduleDay Name + Summer Control Type Day Sch, !- WinterDesignDay ScheduleDay Name + Summer Control Type Day Sch, !- CustomDay1 ScheduleDay Name + Summer Control Type Day Sch; !- CustomDay2 ScheduleDay Name + +Schedule:Week:Daily, + Winter Control Type Week Sch, !- Name + Winter Control Type Day Sch, !- Sunday ScheduleDay Name + Winter Control Type Day Sch, !- Monday ScheduleDay Name + Winter Control Type Day Sch, !- Tuesday ScheduleDay Name + Winter Control Type Day Sch, !- Wednesday ScheduleDay Name + Winter Control Type Day Sch, !- Thursday ScheduleDay Name + Winter Control Type Day Sch, !- Friday ScheduleDay Name + Winter Control Type Day Sch, !- Saturday ScheduleDay Name + Winter Control Type Day Sch, !- Holiday ScheduleDay Name + Winter Control Type Day Sch, !- SummerDesignDay ScheduleDay Name + Winter Control Type Day Sch, !- WinterDesignDay ScheduleDay Name + Winter Control Type Day Sch, !- CustomDay1 ScheduleDay Name + Winter Control Type Day Sch; !- CustomDay2 ScheduleDay Name + +Schedule:Week:Daily, + Summer Control Type Week Sch - cool, !- Name + Summer Control Type Day Sch - cool, !- Sunday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Monday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Tuesday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Wednesday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Thursday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Friday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Saturday ScheduleDay Name + Summer Control Type Day Sch - cool, !- Holiday ScheduleDay Name + Summer Control Type Day Sch - cool, !- SummerDesignDay ScheduleDay Name + Summer Control Type Day Sch - cool, !- WinterDesignDay ScheduleDay Name + Summer Control Type Day Sch - cool, !- CustomDay1 ScheduleDay Name + Summer Control Type Day Sch - cool; !- CustomDay2 ScheduleDay Name + +Schedule:Week:Daily, + Winter Control Type Week Sch - heat, !- Name + Winter Control Type Day Sch - heat, !- Sunday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Monday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Tuesday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Wednesday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Thursday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Friday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Saturday ScheduleDay Name + Winter Control Type Day Sch - heat, !- Holiday ScheduleDay Name + Winter Control Type Day Sch - heat, !- SummerDesignDay ScheduleDay Name + Winter Control Type Day Sch - heat, !- WinterDesignDay ScheduleDay Name + Winter Control Type Day Sch - heat, !- CustomDay1 ScheduleDay Name + Winter Control Type Day Sch - heat; !- CustomDay2 ScheduleDay Name + +Schedule:Year, + Zone Control Type Sched, !- Name + Control Type, !- Schedule Type Limits Name + Winter Control Type Week Sch, !- ScheduleWeek Name 1 + 1, !- Start Month 1 + 1, !- Start Day 1 + 3, !- End Month 1 + 31, !- End Day 1 + Summer Control Type Week Sch, !- ScheduleWeek Name 2 + 4, !- Start Month 2 + 1, !- Start Day 2 + 9, !- End Month 2 + 30, !- End Day 2 + Winter Control Type Week Sch, !- ScheduleWeek Name 3 + 10, !- Start Month 3 + 1, !- Start Day 3 + 12, !- End Month 3 + 31; !- End Day 3 + +Schedule:Compact, + On, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 1; !- Field 4 + +Schedule:Compact, + Off, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 0; !- Field 4 + +Schedule:Compact, + Work efficiency, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 0; !- Field 4 + +Schedule:Compact, + OpaqueShade, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 0; !- Field 4 + +Schedule:Compact, + Zone Comfort Control Type Sched, !- Name + Control Type, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 4; !- Field 4 + +Schedule:Compact, + Design Days Only, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: SummerDesignDay WinterDesignDay, !- Field 2 + Until: 24:00, !- Field 3 + 1, !- Field 4 + For: AllOtherDays, !- Field 5 + Until: 24:00, !- Field 6 + 0; !- Field 7 + +Schedule:Compact, + TypOperativeTempControlSch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: SummerDesignDay WinterDesignDay, !- Field 2 + Until: 24:00, !- Field 3 + 0.0, !- Field 4 + For: AllOtherDays, !- Field 5 + Until: 24:00, !- Field 6 + 0.5; !- Field 7 + +Schedule:Compact, + OnWinterDesignDay, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: WinterDesignDay, !- Field 2 + Until: 24:00, !- Field 3 + 1, !- Field 4 + For: AllOtherDays, !- Field 5 + Until: 24:00, !- Field 6 + 0; !- Field 7 + +Schedule:Compact, + OnSummerDesignDay, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: SummerDesignDay, !- Field 2 + Until: 24:00, !- Field 3 + 1, !- Field 4 + For: AllOtherDays, !- Field 5 + Until: 24:00, !- Field 6 + 0; !- Field 7 + +Schedule:Compact, + On 24/7, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 1; !- Field 4 + +Schedule:Compact, + Office_OpenOff_Occ, !- Name + Fraction, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 07:00, !- Field 3 + 0, !- Field 4 + Until: 08:00, !- Field 5 + 0.25, !- Field 6 + Until: 09:00, !- Field 7 + 0.5, !- Field 8 + Until: 12:00, !- Field 9 + 1, !- Field 10 + Until: 14:00, !- Field 11 + 0.75, !- Field 12 + Until: 17:00, !- Field 13 + 1, !- Field 14 + Until: 18:00, !- Field 15 + 0.5, !- Field 16 + Until: 19:00, !- Field 17 + 0.25, !- Field 18 + Until: 24:00, !- Field 19 + 0, !- Field 20 + For: Weekends, !- Field 21 + Until: 24:00, !- Field 22 + 0, !- Field 23 + For: Holidays, !- Field 24 + Until: 24:00, !- Field 25 + 0, !- Field 26 + For: WinterDesignDay AllOtherDays, !- Field 27 + Until: 24:00, !- Field 28 + 0; !- Field 29 + +Schedule:Compact, + Office_OpenOff_Equip, !- Name + Fraction, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 07:00, !- Field 3 + 0.05394, !- Field 4 + Until: 20:00, !- Field 5 + 1, !- Field 6 + Until: 24:00, !- Field 7 + 0.05394, !- Field 8 + For: Weekends, !- Field 9 + Until: 24:00, !- Field 10 + 0.05394, !- Field 11 + For: Holidays, !- Field 12 + Until: 24:00, !- Field 13 + 0.05394, !- Field 14 + For: WinterDesignDay AllOtherDays, !- Field 15 + Until: 24:00, !- Field 16 + 0; !- Field 17 + +Schedule:Compact, + Office_OpenOff_Light, !- Name + Fraction, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 07:00, !- Field 3 + 0, !- Field 4 + Until: 19:00, !- Field 5 + 1, !- Field 6 + Until: 24:00, !- Field 7 + 0, !- Field 8 + For: Weekends, !- Field 9 + Until: 24:00, !- Field 10 + 0, !- Field 11 + For: Holidays, !- Field 12 + Until: 24:00, !- Field 13 + 0, !- Field 14 + For: WinterDesignDay AllOtherDays, !- Field 15 + Until: 24:00, !- Field 16 + 0; !- Field 17 + +Schedule:Compact, + Default Air Velocity for Comfort Calculations, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 0.1370; !- Field 4 + +Schedule:Compact, + 20001, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: Weekdays Weekends SummerDesignDays, !- Field 2 + Until: 24:00, !- Field 3 + 24, !- Field 4 + For: WinterDesignDays, !- Field 5 + Until: 24:00, !- Field 6 + 0, !- Field 7 + For: AllOtherDays, !- Field 8 + Until: 24:00, !- Field 9 + 24; !- Field 10 + +Schedule:Compact, + Activity Schedule 19, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 110.7; !- Field 4 + +Schedule:Compact, + Clothing Schedule 19, !- Name + Any Number, !- Schedule Type Limits Name + Through: 4/1, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 1, !- Field 4 + Through: 9/30, !- Field 5 + For: AllDays, !- Field 6 + Until: 24:00, !- Field 7 + .5, !- Field 8 + Through: 12/31, !- Field 9 + For: AllDays, !- Field 10 + Until: 24:00, !- Field 11 + 1; !- Field 12 + +Schedule:Compact, + DHW Supply Temperature Block1:Zone2, !- Name + Temperature, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 65; !- Field 4 + +Schedule:Compact, + DHW Mains Temperature Block1:Zone2, !- Name + Temperature, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 10; !- Field 4 + +Schedule:Compact, + Activity Schedule 26, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 110.7; !- Field 4 + +Schedule:Compact, + Clothing Schedule 26, !- Name + Any Number, !- Schedule Type Limits Name + Through: 4/1, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 1, !- Field 4 + Through: 9/30, !- Field 5 + For: AllDays, !- Field 6 + Until: 24:00, !- Field 7 + .5, !- Field 8 + Through: 12/31, !- Field 9 + For: AllDays, !- Field 10 + Until: 24:00, !- Field 11 + 1; !- Field 12 + +Schedule:Compact, + DHW Supply Temperature Block1:Zone1, !- Name + Temperature, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 65; !- Field 4 + +Schedule:Compact, + DHW Mains Temperature Block1:Zone1, !- Name + Temperature, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, !- Field 3 + 10; !- Field 4 + +Schedule:Compact, + Block1:Zone2 Heating Setpoint Schedule, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 12, !- Field 4 + Until: 19:00, !- Field 5 + 22, !- Field 6 + Until: 24:00, !- Field 7 + 12, !- Field 8 + For: WinterDesignDay, !- Field 9 + Until: 24:00, !- Field 10 + 22, !- Field 11 + For: Weekends, !- Field 12 + Until: 24:00, !- Field 13 + 12, !- Field 14 + For: Holidays, !- Field 15 + Until: 24:00, !- Field 16 + 12, !- Field 17 + For: AllOtherDays, !- Field 18 + Until: 24:00, !- Field 19 + -50; !- Field 20 + +Schedule:Compact, + Block1:Zone2 Heating Availability Sch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 1, !- Field 4 + Until: 19:00, !- Field 5 + 1, !- Field 6 + Until: 24:00, !- Field 7 + 1, !- Field 8 + For: WinterDesignDay, !- Field 9 + Until: 24:00, !- Field 10 + 1, !- Field 11 + For: Weekends, !- Field 12 + Until: 24:00, !- Field 13 + 1, !- Field 14 + For: Holidays, !- Field 15 + Until: 24:00, !- Field 16 + 1, !- Field 17 + For: AllOtherDays, !- Field 18 + Until: 24:00, !- Field 19 + 0; !- Field 20 + +Schedule:Compact, + Block1:Zone2 Cooling SP Sch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 100, !- Field 4 + Until: 19:00, !- Field 5 + 24, !- Field 6 + Until: 24:00, !- Field 7 + 100, !- Field 8 + For: Weekends, !- Field 9 + Until: 24:00, !- Field 10 + 100, !- Field 11 + For: Holidays, !- Field 12 + Until: 24:00, !- Field 13 + 100, !- Field 14 + For: WinterDesignDay AllOtherDays, !- Field 15 + Until: 24:00, !- Field 16 + 100; !- Field 17 + +Schedule:Compact, + Block1:Zone2 Cooling Availability Sch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 0, !- Field 4 + Until: 19:00, !- Field 5 + 1, !- Field 6 + Until: 24:00, !- Field 7 + 0, !- Field 8 + For: Weekends, !- Field 9 + Until: 24:00, !- Field 10 + 0, !- Field 11 + For: Holidays, !- Field 12 + Until: 24:00, !- Field 13 + 0, !- Field 14 + For: WinterDesignDay AllOtherDays, !- Field 15 + Until: 24:00, !- Field 16 + 0; !- Field 17 + +Schedule:Compact, + Block1:Zone1 Heating Setpoint Schedule, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 12, !- Field 4 + Until: 19:00, !- Field 5 + 22, !- Field 6 + Until: 24:00, !- Field 7 + 12, !- Field 8 + For: WinterDesignDay, !- Field 9 + Until: 24:00, !- Field 10 + 22, !- Field 11 + For: Weekends, !- Field 12 + Until: 24:00, !- Field 13 + 12, !- Field 14 + For: Holidays, !- Field 15 + Until: 24:00, !- Field 16 + 12, !- Field 17 + For: AllOtherDays, !- Field 18 + Until: 24:00, !- Field 19 + -50; !- Field 20 + +Schedule:Compact, + Block1:Zone1 Heating Availability Sch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 1, !- Field 4 + Until: 19:00, !- Field 5 + 1, !- Field 6 + Until: 24:00, !- Field 7 + 1, !- Field 8 + For: WinterDesignDay, !- Field 9 + Until: 24:00, !- Field 10 + 1, !- Field 11 + For: Weekends, !- Field 12 + Until: 24:00, !- Field 13 + 1, !- Field 14 + For: Holidays, !- Field 15 + Until: 24:00, !- Field 16 + 1, !- Field 17 + For: AllOtherDays, !- Field 18 + Until: 24:00, !- Field 19 + 0; !- Field 20 + +Schedule:Compact, + Block1:Zone1 Cooling SP Sch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 100, !- Field 4 + Until: 19:00, !- Field 5 + 24, !- Field 6 + Until: 24:00, !- Field 7 + 100, !- Field 8 + For: Weekends, !- Field 9 + Until: 24:00, !- Field 10 + 100, !- Field 11 + For: Holidays, !- Field 12 + Until: 24:00, !- Field 13 + 100, !- Field 14 + For: WinterDesignDay AllOtherDays, !- Field 15 + Until: 24:00, !- Field 16 + 100; !- Field 17 + +Schedule:Compact, + Block1:Zone1 Cooling Availability Sch, !- Name + Temperature, !- Schedule Type Limits Name + Through: 31 Dec, !- Field 1 + For: Weekdays SummerDesignDay, !- Field 2 + Until: 05:00, !- Field 3 + 0, !- Field 4 + Until: 19:00, !- Field 5 + 1, !- Field 6 + Until: 24:00, !- Field 7 + 0, !- Field 8 + For: Weekends, !- Field 9 + Until: 24:00, !- Field 10 + 0, !- Field 11 + For: Holidays, !- Field 12 + Until: 24:00, !- Field 13 + 0, !- Field 14 + For: WinterDesignDay AllOtherDays, !- Field 15 + Until: 24:00, !- Field 16 + 0; !- Field 17 + +SCHEDULE:COMPACT, + Control type schedule: Always 4, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,4; !- Field 3 + +SCHEDULE:COMPACT, + Relative humidity setpoint schedule: Always 50.00, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,50; !- Field 3 + +SCHEDULE:COMPACT, + Heating Fanger comfort setpoint: Always -0.5, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,-0.5; !- Field 3 + +SCHEDULE:COMPACT, + Cooling Fanger comfort setpoint: Always 0.1, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, 0.1; !- Field 3 + +SCHEDULE:COMPACT, + Zone CO2 setpoint: Always 900ppm, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, 900; !- Field 3 + +SCHEDULE:COMPACT, + Min CO2 concentration: Always 600ppm, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, 600; !- Field 3 + +SCHEDULE:COMPACT, + Generic contaminant setpoint: Always 0.5ppm, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, 0.5; !- Field 3 + +SCHEDULE:COMPACT, + Air distribution effectiveness (always 1), !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, 1; !- Field 3 + +SCHEDULE:COMPACT, + VRF Heating Cooling (Northern Hemisphere), !- Name + Any Number, !- Schedule Type Limits Name + Through: 31 Mar, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00, 0, !- Field 3 + Through: 30 Sep, !- Field 4 + For: AllDays, !- Field 5 + Until: 24:00, 1, !- Field 6 + Through: 31 Dec, !- Field 7 + For: AllDays, !- Field 8 + Until: 24:00, 0; !- Field 9 + +SCHEDULE:COMPACT, + Vent_SP_temp, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,24; !- Field 3 + +SCHEDULE:COMPACT, + AHST_Sch_BLOCK1_ZONE2, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,20; !- Field 3 + +SCHEDULE:COMPACT, + ACST_Sch_BLOCK1_ZONE2, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,24; !- Field 3 + +SCHEDULE:COMPACT, + AHST_Sch_BLOCK1_ZONE1, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,20; !- Field 3 + +SCHEDULE:COMPACT, + ACST_Sch_BLOCK1_ZONE1, !- Name + Any Number, !- Schedule Type Limits Name + Through: 12/31, !- Field 1 + For: AllDays, !- Field 2 + Until: 24:00,24; !- Field 3 + +Material, + Urea Formaldehyde Foam_.1327, !- Name + Rough, !- Roughness + 0.1327, !- Thickness + 0.04, !- Conductivity + 10, !- Density + 1400, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.6, !- Solar Absorptance + 0.6; !- Visible Absorptance + +Material, + Cast Concrete_.1, !- Name + Rough, !- Roughness + 0.1, !- Thickness + 1.13, !- Conductivity + 2000, !- Density + 1000, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.6, !- Solar Absorptance + 0.6; !- Visible Absorptance + +Material, + Floor/Roof Screed_.07, !- Name + Rough, !- Roughness + 0.07, !- Thickness + 0.41, !- Conductivity + 1200, !- Density + 840, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.73, !- Solar Absorptance + 0.73; !- Visible Absorptance + +Material, + Timber Flooring_.03, !- Name + Rough, !- Roughness + 0.03, !- Thickness + 0.14, !- Conductivity + 650, !- Density + 1200, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.78, !- Solar Absorptance + 0.78; !- Visible Absorptance + +Material, + Asphalt 1_.01, !- Name + Rough, !- Roughness + 0.01, !- Thickness + 0.7, !- Conductivity + 2100, !- Density + 1000, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.85, !- Solar Absorptance + 0.9; !- Visible Absorptance + +Material, + MW Glass Wool (rolls)_.1445, !- Name + Rough, !- Roughness + 0.1445, !- Thickness + 0.04, !- Conductivity + 12, !- Density + 840, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.6, !- Solar Absorptance + 0.6; !- Visible Absorptance + +Material, + Plasterboard_.013, !- Name + Rough, !- Roughness + 0.013, !- Thickness + 0.25, !- Conductivity + 2800, !- Density + 896, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.5, !- Solar Absorptance + 0.5; !- Visible Absorptance + +Material, + Gypsum Plasterboard_.025, !- Name + Rough, !- Roughness + 0.025, !- Thickness + 0.25, !- Conductivity + 900, !- Density + 1000, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.5, !- Solar Absorptance + 0.5; !- Visible Absorptance + +Material, + Brickwork Outer_.1, !- Name + Rough, !- Roughness + 0.1, !- Thickness + 0.84, !- Conductivity + 1700, !- Density + 800, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.7, !- Solar Absorptance + 0.7; !- Visible Absorptance + +Material, + XPS Extruded Polystyrene - CO2 Blowing_.0795, !- Name + Rough, !- Roughness + 0.0795, !- Thickness + 0.034, !- Conductivity + 35, !- Density + 1400, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.6, !- Solar Absorptance + 0.6; !- Visible Absorptance + +Material, + Concrete Block (Medium)_.1, !- Name + Rough, !- Roughness + 0.1, !- Thickness + 0.51, !- Conductivity + 1400, !- Density + 1000, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.6, !- Solar Absorptance + 0.6; !- Visible Absorptance + +Material, + Gypsum Plastering_.013, !- Name + Rough, !- Roughness + 0.013, !- Thickness + 0.4, !- Conductivity + 1000, !- Density + 1000, !- Specific Heat + 0.9, !- Thermal Absorptance + 0.5, !- Solar Absorptance + 0.5; !- Visible Absorptance + +Material:NoMass, + LinearBridgingLayer, !- Name + Rough, !- Roughness + 0.6267, !- Thermal Resistance + 0.01, !- Thermal Absorptance + 0.01, !- Solar Absorptance + 0.01; !- Visible Absorptance + +Material:NoMass, + 3_RVAL_3, !- Name + Rough, !- Roughness + 0.18, !- Thermal Resistance + 0.9, !- Thermal Absorptance + 0.7, !- Solar Absorptance + 0.7; !- Visible Absorptance + +Material:NoMass, + 4_RVAL_2, !- Name + Rough, !- Roughness + 0.18, !- Thermal Resistance + 0.9, !- Thermal Absorptance + 0.7, !- Solar Absorptance + 0.7; !- Visible Absorptance + +Material:NoMass, + 5_RVAL_2, !- Name + Rough, !- Roughness + 0.15, !- Thermal Resistance + 0.9, !- Thermal Absorptance + 0.7, !- Solar Absorptance + 0.7; !- Visible Absorptance + +Material:NoMass, + 6_RVAL_2, !- Name + Rough, !- Roughness + 0.15, !- Thermal Resistance + 0.9, !- Thermal Absorptance + 0.7, !- Solar Absorptance + 0.7; !- Visible Absorptance + +Material:InfraredTransparent, + IRTMaterial; !- Name + +WindowMaterial:Glazing, + 2, !- Name + SpectralAverage, !- Optical Data Type + , !- Window Glass Spectral Data Set Name + 0.003, !- Thickness + 0.837, !- Solar Transmittance at Normal Incidence + 0.075, !- Front Side Solar Reflectance at Normal Incidence + 0.075, !- Back Side Solar Reflectance at Normal Incidence + 0.898, !- Visible Transmittance at Normal Incidence + 0.081, !- Front Side Visible Reflectance at Normal Incidence + 0.081, !- Back Side Visible Reflectance at Normal Incidence + 0, !- Infrared Transmittance at Normal Incidence + 0.84, !- Front Side Infrared Hemispherical Emissivity + 0.84, !- Back Side Infrared Hemispherical Emissivity + 0.9, !- Conductivity + 1; !- Dirt Correction Factor for Solar and Visible Transmittance + +WindowMaterial:Glazing, + 38, !- Name + SpectralAverage, !- Optical Data Type + , !- Window Glass Spectral Data Set Name + 0.003, !- Thickness + 0.74, !- Solar Transmittance at Normal Incidence + 0.09, !- Front Side Solar Reflectance at Normal Incidence + 0.1, !- Back Side Solar Reflectance at Normal Incidence + 0.82, !- Visible Transmittance at Normal Incidence + 0.11, !- Front Side Visible Reflectance at Normal Incidence + 0.12, !- Back Side Visible Reflectance at Normal Incidence + 0, !- Infrared Transmittance at Normal Incidence + 0.84, !- Front Side Infrared Hemispherical Emissivity + 0.2, !- Back Side Infrared Hemispherical Emissivity + 0.9, !- Conductivity + 1; !- Dirt Correction Factor for Solar and Visible Transmittance + +WindowMaterial:Gas, + 1002, !- Name + Air, !- Gas Type + 0.013; !- Thickness + +WindowMaterial:Gas, + Half thickness 1002, !- Name + Air, !- Gas Type + 0.0065; !- Thickness + +Construction, + LinearBridgingConstruction, !- Name + LinearBridgingLayer; !- Outside Layer + +Construction, + IRTSurface, !- Name + IRTMaterial; !- Outside Layer + +Construction, + Project ground floor, !- Name + Urea Formaldehyde Foam_.1327, !- Outside Layer + Cast Concrete_.1, !- Layer 2 + Floor/Roof Screed_.07, !- Layer 3 + Timber Flooring_.03; !- Layer 4 + +Construction, + Project ground floor_Rev, !- Name + Timber Flooring_.03, !- Outside Layer + Floor/Roof Screed_.07, !- Layer 2 + Cast Concrete_.1, !- Layer 3 + Urea Formaldehyde Foam_.1327; !- Layer 4 + +Construction, + Project flat roof, !- Name + Asphalt 1_.01, !- Outside Layer + MW Glass Wool (rolls)_.1445, !- Layer 2 + 3_RVAL_3, !- Layer 3 + Plasterboard_.013; !- Layer 4 + +Construction, + Project flat roof_Rev, !- Name + Plasterboard_.013, !- Outside Layer + 4_RVAL_2, !- Layer 2 + MW Glass Wool (rolls)_.1445, !- Layer 3 + Asphalt 1_.01; !- Layer 4 + +Construction, + Project partition, !- Name + Gypsum Plasterboard_.025, !- Outside Layer + 5_RVAL_2, !- Layer 2 + Gypsum Plasterboard_.025; !- Layer 3 + +Construction, + Project partition_Rev, !- Name + Gypsum Plasterboard_.025, !- Outside Layer + 6_RVAL_2, !- Layer 2 + Gypsum Plasterboard_.025; !- Layer 3 + +Construction, + Project wall, !- Name + Brickwork Outer_.1, !- Outside Layer + XPS Extruded Polystyrene - CO2 Blowing_.0795, !- Layer 2 + Concrete Block (Medium)_.1, !- Layer 3 + Gypsum Plastering_.013; !- Layer 4 + +Construction, + Project wall_Rev, !- Name + Gypsum Plastering_.013, !- Outside Layer + Concrete Block (Medium)_.1, !- Layer 2 + XPS Extruded Polystyrene - CO2 Blowing_.0795, !- Layer 3 + Brickwork Outer_.1; !- Layer 4 + +Construction, + Project external glazing - 1001, !- Name + 38, !- Outside Layer + 1002, !- Layer 2 + 2; !- Layer 3 + +GlobalGeometryRules, + LowerLeftCorner, !- Starting Vertex Position + CounterClockWise, !- Vertex Entry Direction + Relative; !- Coordinate System + +Zone, + Block1:Zone2, !- Name + 0, !- Direction of Relative North + 0, !- X Origin + 0, !- Y Origin + 0, !- Z Origin + 1, !- Type + 1, !- Multiplier + , !- Ceiling Height + 126.7064, !- Volume + 36.2018, !- Floor Area + TARP, !- Zone Inside Convection Algorithm + , !- Zone Outside Convection Algorithm + Yes; !- Part of Total Floor Area + +Zone, + Block1:Zone1, !- Name + 0, !- Direction of Relative North + 0, !- X Origin + 0, !- Y Origin + 0, !- Z Origin + 1, !- Type + 1, !- Multiplier + , !- Ceiling Height + 105.3337, !- Volume + 30.0953, !- Floor Area + TARP, !- Zone Inside Convection Algorithm + , !- Zone Outside Convection Algorithm + Yes; !- Part of Total Floor Area + +BuildingSurface:Detailed, + Block1:Zone2_GroundFloor_0_0_0, !- Name + Floor, !- Surface Type + Project ground floor, !- Construction Name + Block1:Zone2, !- Zone Name + , !- Space Name + Ground, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + NoSun, !- Sun Exposure + NoWind, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 2.014754282, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + -5.298249883, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + -5.298249883, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 0, !- Vertex 3 Zcoordinate + 2.014754282, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 0; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone2_Roof_1_0_0, !- Name + Roof, !- Surface Type + Project flat roof, !- Construction Name + Block1:Zone2, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + -5.298249883, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 3.5, !- Vertex 1 Zcoordinate + 2.014754282, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 3.5, !- Vertex 2 Zcoordinate + 2.014754282, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + -5.298249883, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone2_Partition_2_0_0, !- Name + Wall, !- Surface Type + Project partition, !- Construction Name + Block1:Zone2, !- Zone Name + , !- Space Name + Surface, !- Outside Boundary Condition + Block1:Zone1_Partition_4_0_10000, !- Outside Boundary Condition Object + NoSun, !- Sun Exposure + NoWind, !- Wind Exposure + 0, !- View Factor to Ground + 4, !- Number of Vertices + 2.014754282, !- Vertex 1 Xcoordinate + -3.76053816, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 2.014754282, !- Vertex 2 Xcoordinate + 2.0367289, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 2.014754282, !- Vertex 3 Xcoordinate + 2.0367289, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 2.014754282, !- Vertex 4 Xcoordinate + -3.76053816, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone1_Partition_4_0_10000, !- Name + Wall, !- Surface Type + Project partition_Rev, !- Construction Name + Block1:Zone1, !- Zone Name + , !- Space Name + Surface, !- Outside Boundary Condition + Block1:Zone2_Partition_2_0_0, !- Outside Boundary Condition Object + NoSun, !- Sun Exposure + NoWind, !- Wind Exposure + 0, !- View Factor to Ground + 4, !- Number of Vertices + 2.014754282, !- Vertex 1 Xcoordinate + 2.0367289, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 2.014754282, !- Vertex 2 Xcoordinate + -3.76053816, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 2.014754282, !- Vertex 3 Xcoordinate + -3.76053816, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 2.014754282, !- Vertex 4 Xcoordinate + 2.0367289, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone2_Wall_3_0_0, !- Name + Wall, !- Surface Type + Project wall, !- Construction Name + Block1:Zone2, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 2.014754282, !- Vertex 1 Xcoordinate + 2.0367288965, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + -5.298249883, !- Vertex 2 Xcoordinate + 2.0367288965, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + -5.298249883, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 2.014754282, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone2_Wall_4_0_0, !- Name + Wall, !- Surface Type + Project wall, !- Construction Name + Block1:Zone2, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + -5.298249883, !- Vertex 1 Xcoordinate + 2.0367288965, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + -5.298249883, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + -5.298249883, !- Vertex 3 Xcoordinate + -3.7605381575, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + -5.298249883, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone2_Wall_5_0_0, !- Name + Wall, !- Surface Type + Project wall, !- Construction Name + Block1:Zone2, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + -5.298249883, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 2.014754282, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 2.014754282, !- Vertex 3 Xcoordinate + -3.7605381575, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + -5.298249883, !- Vertex 4 Xcoordinate + -3.7605381575, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone1_GroundFloor_0_0_0, !- Name + Floor, !- Surface Type + Project ground floor, !- Construction Name + Block1:Zone1, !- Zone Name + , !- Space Name + Ground, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + NoSun, !- Sun Exposure + NoWind, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 8.156199441, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 2.014754282, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 2.014754282, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 0, !- Vertex 3 Zcoordinate + 8.156199441, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 0; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone1_Roof_1_0_0, !- Name + Roof, !- Surface Type + Project flat roof, !- Construction Name + Block1:Zone1, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 2.014754282, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 3.5, !- Vertex 1 Zcoordinate + 8.156199441, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 3.5, !- Vertex 2 Zcoordinate + 8.156199441, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 2.014754282, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone1_Wall_2_0_0, !- Name + Wall, !- Surface Type + Project wall, !- Construction Name + Block1:Zone1, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 8.156199441, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 8.156199441, !- Vertex 2 Xcoordinate + 2.0367288965, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 8.156199441, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 8.156199441, !- Vertex 4 Xcoordinate + -3.7605381575, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone1_Wall_3_0_0, !- Name + Wall, !- Surface Type + Project wall, !- Construction Name + Block1:Zone1, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 8.156199441, !- Vertex 1 Xcoordinate + 2.0367288965, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 2.014754282, !- Vertex 2 Xcoordinate + 2.0367288965, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 2.014754282, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 8.156199441, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +BuildingSurface:Detailed, + Block1:Zone1_Wall_5_0_0, !- Name + Wall, !- Surface Type + Project wall, !- Construction Name + Block1:Zone1, !- Zone Name + , !- Space Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + AutoCalculate, !- View Factor to Ground + 4, !- Number of Vertices + 2.014754282, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0, !- Vertex 1 Zcoordinate + 8.156199441, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0, !- Vertex 2 Zcoordinate + 8.156199441, !- Vertex 3 Xcoordinate + -3.7605381575, !- Vertex 3 Ycoordinate + 3.5, !- Vertex 3 Zcoordinate + 2.014754282, !- Vertex 4 Xcoordinate + -3.7605381575, !- Vertex 4 Ycoordinate + 3.5; !- Vertex 4 Zcoordinate + +FenestrationSurface:Detailed, + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Name + Window, !- Surface Type + Project external glazing - 1001, !- Construction Name + Block1:Zone2_Wall_3_0_0, !- Building Surface Name + , !- Outside Boundary Condition Object + AutoCalculate, !- View Factor to Ground + 1, !- Frame and Divider Name + 1, !- Multiplier + 4, !- Number of Vertices + 0.857928656, !- Vertex 1 Xcoordinate + 2.0367288965, !- Vertex 1 Ycoordinate + 0.84, !- Vertex 1 Zcoordinate + -3.923924256, !- Vertex 2 Xcoordinate + 2.0367288965, !- Vertex 2 Ycoordinate + 0.84, !- Vertex 2 Zcoordinate + -3.923924256, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 2.26, !- Vertex 3 Zcoordinate + 0.857928656, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 2.26; !- Vertex 4 Zcoordinate + +FenestrationSurface:Detailed, + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Name + Window, !- Surface Type + Project external glazing - 1001, !- Construction Name + Block1:Zone2_Wall_4_0_0, !- Building Surface Name + , !- Outside Boundary Condition Object + AutoCalculate, !- View Factor to Ground + 1, !- Frame and Divider Name + 1, !- Multiplier + 4, !- Number of Vertices + -5.298249883, !- Vertex 1 Xcoordinate + 0.922388841, !- Vertex 1 Ycoordinate + 0.84, !- Vertex 1 Zcoordinate + -5.298249883, !- Vertex 2 Xcoordinate + -2.646198101, !- Vertex 2 Ycoordinate + 0.84, !- Vertex 2 Zcoordinate + -5.298249883, !- Vertex 3 Xcoordinate + -2.646198101, !- Vertex 3 Ycoordinate + 2.26, !- Vertex 3 Zcoordinate + -5.298249883, !- Vertex 4 Xcoordinate + 0.922388841, !- Vertex 4 Ycoordinate + 2.26; !- Vertex 4 Zcoordinate + +FenestrationSurface:Detailed, + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Name + Window, !- Surface Type + Project external glazing - 1001, !- Construction Name + Block1:Zone2_Wall_5_0_0, !- Building Surface Name + , !- Outside Boundary Condition Object + AutoCalculate, !- View Factor to Ground + 1, !- Frame and Divider Name + 1, !- Multiplier + 4, !- Number of Vertices + -3.923924256, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0.84, !- Vertex 1 Zcoordinate + 0.857928656, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0.84, !- Vertex 2 Zcoordinate + 0.857928656, !- Vertex 3 Xcoordinate + -3.7605381575, !- Vertex 3 Ycoordinate + 2.26, !- Vertex 3 Zcoordinate + -3.923924256, !- Vertex 4 Xcoordinate + -3.7605381575, !- Vertex 4 Ycoordinate + 2.26; !- Vertex 4 Zcoordinate + +FenestrationSurface:Detailed, + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Name + Window, !- Surface Type + Project external glazing - 1001, !- Construction Name + Block1:Zone1_Wall_2_0_0, !- Building Surface Name + , !- Outside Boundary Condition Object + AutoCalculate, !- View Factor to Ground + 1, !- Frame and Divider Name + 1, !- Multiplier + 4, !- Number of Vertices + 8.156199441, !- Vertex 1 Xcoordinate + -2.646198101, !- Vertex 1 Ycoordinate + 0.84, !- Vertex 1 Zcoordinate + 8.156199441, !- Vertex 2 Xcoordinate + 0.922388841, !- Vertex 2 Ycoordinate + 0.84, !- Vertex 2 Zcoordinate + 8.156199441, !- Vertex 3 Xcoordinate + 0.922388841, !- Vertex 3 Ycoordinate + 2.26, !- Vertex 3 Zcoordinate + 8.156199441, !- Vertex 4 Xcoordinate + -2.646198101, !- Vertex 4 Ycoordinate + 2.26; !- Vertex 4 Zcoordinate + +FenestrationSurface:Detailed, + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Name + Window, !- Surface Type + Project external glazing - 1001, !- Construction Name + Block1:Zone1_Wall_3_0_0, !- Building Surface Name + , !- Outside Boundary Condition Object + AutoCalculate, !- View Factor to Ground + 1, !- Frame and Divider Name + 1, !- Multiplier + 4, !- Number of Vertices + 6.957607666, !- Vertex 1 Xcoordinate + 2.0367288965, !- Vertex 1 Ycoordinate + 0.84, !- Vertex 1 Zcoordinate + 2.995846054, !- Vertex 2 Xcoordinate + 2.0367288965, !- Vertex 2 Ycoordinate + 0.84, !- Vertex 2 Zcoordinate + 2.995846054, !- Vertex 3 Xcoordinate + 2.0367288965, !- Vertex 3 Ycoordinate + 2.26, !- Vertex 3 Zcoordinate + 6.957607666, !- Vertex 4 Xcoordinate + 2.0367288965, !- Vertex 4 Ycoordinate + 2.26; !- Vertex 4 Zcoordinate + +FenestrationSurface:Detailed, + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Name + Window, !- Surface Type + Project external glazing - 1001, !- Construction Name + Block1:Zone1_Wall_5_0_0, !- Building Surface Name + , !- Outside Boundary Condition Object + AutoCalculate, !- View Factor to Ground + 1, !- Frame and Divider Name + 1, !- Multiplier + 4, !- Number of Vertices + 2.995846054, !- Vertex 1 Xcoordinate + -3.7605381575, !- Vertex 1 Ycoordinate + 0.84, !- Vertex 1 Zcoordinate + 6.957607666, !- Vertex 2 Xcoordinate + -3.7605381575, !- Vertex 2 Ycoordinate + 0.84, !- Vertex 2 Zcoordinate + 6.957607666, !- Vertex 3 Xcoordinate + -3.7605381575, !- Vertex 3 Ycoordinate + 2.26, !- Vertex 3 Zcoordinate + 2.995846054, !- Vertex 4 Xcoordinate + -3.7605381575, !- Vertex 4 Ycoordinate + 2.26; !- Vertex 4 Zcoordinate + +WindowProperty:FrameAndDivider, + 1, !- Name + 0.04, !- Frame Width + 0, !- Frame Outside Projection + 0, !- Frame Inside Projection + 9.5, !- Frame Conductance + 1, !- Ratio of FrameEdge Glass Conductance to CenterOfGlass Conductance + 0.5, !- Frame Solar Absorptance + 0.5, !- Frame Visible Absorptance + 0.9, !- Frame Thermal Hemispherical Emissivity + DividedLite, !- Divider Type + 0.02, !- Divider Width + 1, !- Number of Horizontal Dividers + 1, !- Number of Vertical Dividers + 0, !- Divider Outside Projection + 0, !- Divider Inside Projection + 9.5, !- Divider Conductance + 1, !- Ratio of DividerEdge Glass Conductance to CenterOfGlass Conductance + 0.5, !- Divider Solar Absorptance + 0.5, !- Divider Visible Absorptance + 0.9, !- Divider Thermal Hemispherical Emissivity + 0.5, !- Outside Reveal Solar Absorptance + 0, !- Inside Sill Depth + 0.5, !- Inside Sill Solar Absorptance + 0.0, !- Inside Reveal Depth + 0.5, !- Inside Reveal Solar Absorptance + CurtainWall; !- NFRC Product Type for Assembly Calculations + +PEOPLE, + People Block1:Zone2, !- Name + Block1:Zone2, !- Zone or ZoneList or Space or SpaceList Name + On 24/7, !- Number of People Schedule Name + People/Area, !- Number of People Calculation Method + , !- Number of People + 0.111, !- People per Floor Area + , !- Floor Area per Person + 0.3, !- Fraction Radiant + AutoCalculate, !- Sensible Heat Fraction + Activity Schedule 19, !- Activity Level Schedule Name + 3.82e-08, !- Carbon Dioxide Generation Rate + No, !- Enable ASHRAE 55 Comfort Warnings + ZoneAveraged, !- Mean Radiant Temperature Calculation Type + , !- Surface NameAngle Factor List Name + Work efficiency, !- Work Efficiency Schedule Name + ClothingInsulationSchedule, !- Clothing Insulation Calculation Method + , !- Clothing Insulation Calculation Method Schedule Name + Clothing Schedule 19, !- Clothing Insulation Schedule Name + Default Air Velocity for Comfort Calculations, !- Air Velocity Schedule Name + AdaptiveASH55, !- Thermal Comfort Model 1 Type + AdaptiveCEN15251, !- Thermal Comfort Model 2 Type + , !- Thermal Comfort Model 3 Type + , !- Thermal Comfort Model 4 Type + , !- Thermal Comfort Model 5 Type + , !- Thermal Comfort Model 6 Type + , !- Thermal Comfort Model 7 Type + , !- Ankle Level Air Velocity Schedule Name + 15.56, !- Cold Stress Temperature Threshold + 30; !- Heat Stress Temperature Threshold + +PEOPLE, + People Block1:Zone1, !- Name + Block1:Zone1, !- Zone or ZoneList or Space or SpaceList Name + On 24/7, !- Number of People Schedule Name + People/Area, !- Number of People Calculation Method + , !- Number of People + 0.111, !- People per Floor Area + , !- Floor Area per Person + 0.3, !- Fraction Radiant + AutoCalculate, !- Sensible Heat Fraction + Activity Schedule 26, !- Activity Level Schedule Name + 3.82e-08, !- Carbon Dioxide Generation Rate + No, !- Enable ASHRAE 55 Comfort Warnings + ZoneAveraged, !- Mean Radiant Temperature Calculation Type + , !- Surface NameAngle Factor List Name + Work efficiency, !- Work Efficiency Schedule Name + ClothingInsulationSchedule, !- Clothing Insulation Calculation Method + , !- Clothing Insulation Calculation Method Schedule Name + Clothing Schedule 26, !- Clothing Insulation Schedule Name + Default Air Velocity for Comfort Calculations, !- Air Velocity Schedule Name + AdaptiveASH55, !- Thermal Comfort Model 1 Type + AdaptiveCEN15251, !- Thermal Comfort Model 2 Type + , !- Thermal Comfort Model 3 Type + , !- Thermal Comfort Model 4 Type + , !- Thermal Comfort Model 5 Type + , !- Thermal Comfort Model 6 Type + , !- Thermal Comfort Model 7 Type + , !- Ankle Level Air Velocity Schedule Name + 15.56, !- Cold Stress Temperature Threshold + 30; !- Heat Stress Temperature Threshold + +Lights, + Block1:Zone2 General lighting, !- Name + Block1:Zone2, !- Zone or ZoneList or Space or SpaceList Name + Office_OpenOff_Light, !- Schedule Name + Watts/Area, !- Design Level Calculation Method + , !- Lighting Level + 20, !- Watts per Zone Floor Area + , !- Watts per Person + 0, !- Return Air Fraction + 0.42, !- Fraction Radiant + 0.18, !- Fraction Visible + 1, !- Fraction Replaceable + ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights; !- EndUse Subcategory + +Lights, + Block1:Zone1 General lighting, !- Name + Block1:Zone1, !- Zone or ZoneList or Space or SpaceList Name + Office_OpenOff_Light, !- Schedule Name + Watts/Area, !- Design Level Calculation Method + , !- Lighting Level + 20, !- Watts per Zone Floor Area + , !- Watts per Person + 0, !- Return Air Fraction + 0.42, !- Fraction Radiant + 0.18, !- Fraction Visible + 1, !- Fraction Replaceable + ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights; !- EndUse Subcategory + +OtherEquipment, + Block1:Zone2 Equipment gain, !- Name + Electricity, !- Fuel Type + Block1:Zone2, !- Zone or ZoneList or Space or SpaceList Name + Office_OpenOff_Equip, !- Schedule Name + Watts/Area, !- Design Level Calculation Method + , !- Design Level + 11.77, !- Power per Zone Floor Area + , !- Power per Person + 0, !- Fraction Latent + 0.2, !- Fraction Radiant + 0, !- Fraction Lost + 0, !- Carbon Dioxide Generation Rate + General; !- EndUse Subcategory + +OtherEquipment, + Block1:Zone1 Equipment gain, !- Name + Electricity, !- Fuel Type + Block1:Zone1, !- Zone or ZoneList or Space or SpaceList Name + Office_OpenOff_Equip, !- Schedule Name + Watts/Area, !- Design Level Calculation Method + , !- Design Level + 11.77, !- Power per Zone Floor Area + , !- Power per Person + 0, !- Fraction Latent + 0.2, !- Fraction Radiant + 0, !- Fraction Lost + 0, !- Carbon Dioxide Generation Rate + General; !- EndUse Subcategory + +OutputControl:IlluminanceMap:Style, + Comma; !- Column Separator + +AirflowNetwork:SimulationControl, + VENT, !- Name + MultizoneWithoutDistribution, !- AirflowNetwork Control + Input, !- Wind Pressure Coefficient Type + OpeningHeight, !- Height Selection for Local Wind Pressure Calculation + LowRise, !- Building Type + 1000, !- Maximum Number of Iterations + ZeroNodePressures, !- Initialization Type + 0.0001, !- Relative Airflow Convergence Tolerance + 1e-06, !- Absolute Airflow Convergence Tolerance + -0.5, !- Convergence Acceleration Limit + 0, !- Azimuth Angle of Long Axis of Building + 1, !- Ratio of Building Width Along Short Axis to Width Along Long Axis + No, !- Height Dependence of External Node Temperature + SkylineLU, !- Solver + No, !- Allow Unsupported Zone Equipment + No; !- Do Distribution Duct Sizing Calculation + +AirflowNetwork:MultiZone:Zone, + Block1:Zone2, !- Zone Name + Temperature, !- Ventilation Control Mode + 20001, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On 24/7; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Zone, + Block1:Zone1, !- Zone Name + Temperature, !- Ventilation Control Mode + 20001, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On 24/7; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Roof_1_0_0, !- Surface Name + Block1:Zone2_Roof_1_0_0, !- Leakage Component Name + Block1:Zone2_Roof_1_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Partition_2_0_0, !- Surface Name + Block1:Zone2_Partition_2_0_0, !- Leakage Component Name + , !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Wall_3_0_0, !- Surface Name + Block1:Zone2_Wall_3_0_0, !- Leakage Component Name + Block1:Zone2_Wall_3_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Surface Name + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Leakage Component Name + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- External Node Name + 1, !- WindowDoor Opening Factor or Crack Factor + Temperature, !- Ventilation Control Mode + Vent_SP_temp, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Wall_4_0_0, !- Surface Name + Block1:Zone2_Wall_4_0_0, !- Leakage Component Name + Block1:Zone2_Wall_4_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Surface Name + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Leakage Component Name + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- External Node Name + 1, !- WindowDoor Opening Factor or Crack Factor + Temperature, !- Ventilation Control Mode + Vent_SP_temp, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Wall_5_0_0, !- Surface Name + Block1:Zone2_Wall_5_0_0, !- Leakage Component Name + Block1:Zone2_Wall_5_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Surface Name + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Leakage Component Name + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- External Node Name + 1, !- WindowDoor Opening Factor or Crack Factor + Temperature, !- Ventilation Control Mode + Vent_SP_temp, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Roof_1_0_0, !- Surface Name + Block1:Zone1_Roof_1_0_0, !- Leakage Component Name + Block1:Zone1_Roof_1_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Wall_2_0_0, !- Surface Name + Block1:Zone1_Wall_2_0_0, !- Leakage Component Name + Block1:Zone1_Wall_2_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Surface Name + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Leakage Component Name + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- External Node Name + 1, !- WindowDoor Opening Factor or Crack Factor + Temperature, !- Ventilation Control Mode + Vent_SP_temp, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Wall_3_0_0, !- Surface Name + Block1:Zone1_Wall_3_0_0, !- Leakage Component Name + Block1:Zone1_Wall_3_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Surface Name + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Leakage Component Name + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- External Node Name + 1, !- WindowDoor Opening Factor or Crack Factor + Temperature, !- Ventilation Control Mode + Vent_SP_temp, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Wall_5_0_0, !- Surface Name + Block1:Zone1_Wall_5_0_0, !- Leakage Component Name + Block1:Zone1_Wall_5_0_0, !- External Node Name + 1; !- WindowDoor Opening Factor or Crack Factor + +AirflowNetwork:MultiZone:Surface, + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Surface Name + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Leakage Component Name + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- External Node Name + 1, !- WindowDoor Opening Factor or Crack Factor + Temperature, !- Ventilation Control Mode + Vent_SP_temp, !- Ventilation Control Zone Temperature Setpoint Schedule Name + , !- Minimum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Temperature Difference Upper Limit for Minimum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Lower Limit For Maximum Venting Open Factor + , !- Indoor and Outdoor Enthalpy Difference Upper Limit for Minimum Venting Open Factor + On; !- Venting Availability Schedule Name + +AirflowNetwork:MultiZone:ReferenceCrackConditions, + Reference Crack Conditions, !- Name + 20, !- Reference Temperature + 101320, !- Reference Barometric Pressure + 0.005; !- Reference Humidity Ratio + +AirflowNetwork:MultiZone:Surface:Crack, + DefaultCrack, !- Name + 1e-11, !- Air Mass Flow Coefficient at Reference Conditions + 0.66, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone2_Roof_1_0_0, !- Name + 0.0042395, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone2_Partition_2_0_0, !- Name + 0.0608713, !- Air Mass Flow Coefficient at Reference Conditions + 0.75, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone2_Wall_3_0_0, !- Name + 0.0018805, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone2_Wall_4_0_0, !- Name + 0.0015223, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone2_Wall_5_0_0, !- Name + 0.0018805, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone1_Roof_1_0_0, !- Name + 0.0035604, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone1_Wall_2_0_0, !- Name + 0.0015223, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone1_Wall_3_0_0, !- Name + 0.0015869, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Surface:Crack, + Block1:Zone1_Wall_5_0_0, !- Name + 0.0015869, !- Air Mass Flow Coefficient at Reference Conditions + 0.7, !- Air Mass Flow Exponent + Reference Crack Conditions; !- Reference Crack Conditions + +AirflowNetwork:MultiZone:Component:DetailedOpening, + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Name + 0.00014, !- Air Mass Flow Coefficient When Opening is Closed + 0.65, !- Air Mass Flow Exponent When Opening is Closed + NonPivoted, !- Type of Rectangular Large Vertical Opening LVO + 0, !- Extra Crack Length or Height of Pivoting Axis + 2, !- Number of Sets of Opening Factor Data + 0, !- Opening Factor 1 + 0.65, !- Discharge Coefficient for Opening Factor 1 + 0, !- Width Factor for Opening Factor 1 + 1, !- Height Factor for Opening Factor 1 + 0, !- Start Height Factor for Opening Factor 1 + 1, !- Opening Factor 2 + 0.65, !- Discharge Coefficient for Opening Factor 2 + 1, !- Width Factor for Opening Factor 2 + 1, !- Height Factor for Opening Factor 2 + 0, !- Start Height Factor for Opening Factor 2 + 0, !- Opening Factor 3 + 0, !- Discharge Coefficient for Opening Factor 3 + 0, !- Width Factor for Opening Factor 3 + 0, !- Height Factor for Opening Factor 3 + 0, !- Start Height Factor for Opening Factor 3 + 0, !- Opening Factor 4 + 0, !- Discharge Coefficient for Opening Factor 4 + 0, !- Width Factor for Opening Factor 4 + 0, !- Height Factor for Opening Factor 4 + 0; !- Start Height Factor for Opening Factor 4 + +AirflowNetwork:MultiZone:Component:DetailedOpening, + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Name + 0.00014, !- Air Mass Flow Coefficient When Opening is Closed + 0.65, !- Air Mass Flow Exponent When Opening is Closed + NonPivoted, !- Type of Rectangular Large Vertical Opening LVO + 0, !- Extra Crack Length or Height of Pivoting Axis + 2, !- Number of Sets of Opening Factor Data + 0, !- Opening Factor 1 + 0.65, !- Discharge Coefficient for Opening Factor 1 + 0, !- Width Factor for Opening Factor 1 + 1, !- Height Factor for Opening Factor 1 + 0, !- Start Height Factor for Opening Factor 1 + 1, !- Opening Factor 2 + 0.65, !- Discharge Coefficient for Opening Factor 2 + 1, !- Width Factor for Opening Factor 2 + 1, !- Height Factor for Opening Factor 2 + 0, !- Start Height Factor for Opening Factor 2 + 0, !- Opening Factor 3 + 0, !- Discharge Coefficient for Opening Factor 3 + 0, !- Width Factor for Opening Factor 3 + 0, !- Height Factor for Opening Factor 3 + 0, !- Start Height Factor for Opening Factor 3 + 0, !- Opening Factor 4 + 0, !- Discharge Coefficient for Opening Factor 4 + 0, !- Width Factor for Opening Factor 4 + 0, !- Height Factor for Opening Factor 4 + 0; !- Start Height Factor for Opening Factor 4 + +AirflowNetwork:MultiZone:Component:DetailedOpening, + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Name + 0.00014, !- Air Mass Flow Coefficient When Opening is Closed + 0.65, !- Air Mass Flow Exponent When Opening is Closed + NonPivoted, !- Type of Rectangular Large Vertical Opening LVO + 0, !- Extra Crack Length or Height of Pivoting Axis + 2, !- Number of Sets of Opening Factor Data + 0, !- Opening Factor 1 + 0.65, !- Discharge Coefficient for Opening Factor 1 + 0, !- Width Factor for Opening Factor 1 + 1, !- Height Factor for Opening Factor 1 + 0, !- Start Height Factor for Opening Factor 1 + 1, !- Opening Factor 2 + 0.65, !- Discharge Coefficient for Opening Factor 2 + 1, !- Width Factor for Opening Factor 2 + 1, !- Height Factor for Opening Factor 2 + 0, !- Start Height Factor for Opening Factor 2 + 0, !- Opening Factor 3 + 0, !- Discharge Coefficient for Opening Factor 3 + 0, !- Width Factor for Opening Factor 3 + 0, !- Height Factor for Opening Factor 3 + 0, !- Start Height Factor for Opening Factor 3 + 0, !- Opening Factor 4 + 0, !- Discharge Coefficient for Opening Factor 4 + 0, !- Width Factor for Opening Factor 4 + 0, !- Height Factor for Opening Factor 4 + 0; !- Start Height Factor for Opening Factor 4 + +AirflowNetwork:MultiZone:Component:DetailedOpening, + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Name + 0.00014, !- Air Mass Flow Coefficient When Opening is Closed + 0.65, !- Air Mass Flow Exponent When Opening is Closed + NonPivoted, !- Type of Rectangular Large Vertical Opening LVO + 0, !- Extra Crack Length or Height of Pivoting Axis + 2, !- Number of Sets of Opening Factor Data + 0, !- Opening Factor 1 + 0.65, !- Discharge Coefficient for Opening Factor 1 + 0, !- Width Factor for Opening Factor 1 + 1, !- Height Factor for Opening Factor 1 + 0, !- Start Height Factor for Opening Factor 1 + 1, !- Opening Factor 2 + 0.65, !- Discharge Coefficient for Opening Factor 2 + 1, !- Width Factor for Opening Factor 2 + 1, !- Height Factor for Opening Factor 2 + 0, !- Start Height Factor for Opening Factor 2 + 0, !- Opening Factor 3 + 0, !- Discharge Coefficient for Opening Factor 3 + 0, !- Width Factor for Opening Factor 3 + 0, !- Height Factor for Opening Factor 3 + 0, !- Start Height Factor for Opening Factor 3 + 0, !- Opening Factor 4 + 0, !- Discharge Coefficient for Opening Factor 4 + 0, !- Width Factor for Opening Factor 4 + 0, !- Height Factor for Opening Factor 4 + 0; !- Start Height Factor for Opening Factor 4 + +AirflowNetwork:MultiZone:Component:DetailedOpening, + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Name + 0.00014, !- Air Mass Flow Coefficient When Opening is Closed + 0.65, !- Air Mass Flow Exponent When Opening is Closed + NonPivoted, !- Type of Rectangular Large Vertical Opening LVO + 0, !- Extra Crack Length or Height of Pivoting Axis + 2, !- Number of Sets of Opening Factor Data + 0, !- Opening Factor 1 + 0.65, !- Discharge Coefficient for Opening Factor 1 + 0, !- Width Factor for Opening Factor 1 + 1, !- Height Factor for Opening Factor 1 + 0, !- Start Height Factor for Opening Factor 1 + 1, !- Opening Factor 2 + 0.65, !- Discharge Coefficient for Opening Factor 2 + 1, !- Width Factor for Opening Factor 2 + 1, !- Height Factor for Opening Factor 2 + 0, !- Start Height Factor for Opening Factor 2 + 0, !- Opening Factor 3 + 0, !- Discharge Coefficient for Opening Factor 3 + 0, !- Width Factor for Opening Factor 3 + 0, !- Height Factor for Opening Factor 3 + 0, !- Start Height Factor for Opening Factor 3 + 0, !- Opening Factor 4 + 0, !- Discharge Coefficient for Opening Factor 4 + 0, !- Width Factor for Opening Factor 4 + 0, !- Height Factor for Opening Factor 4 + 0; !- Start Height Factor for Opening Factor 4 + +AirflowNetwork:MultiZone:Component:DetailedOpening, + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Name + 0.00014, !- Air Mass Flow Coefficient When Opening is Closed + 0.65, !- Air Mass Flow Exponent When Opening is Closed + NonPivoted, !- Type of Rectangular Large Vertical Opening LVO + 0, !- Extra Crack Length or Height of Pivoting Axis + 2, !- Number of Sets of Opening Factor Data + 0, !- Opening Factor 1 + 0.65, !- Discharge Coefficient for Opening Factor 1 + 0, !- Width Factor for Opening Factor 1 + 1, !- Height Factor for Opening Factor 1 + 0, !- Start Height Factor for Opening Factor 1 + 1, !- Opening Factor 2 + 0.65, !- Discharge Coefficient for Opening Factor 2 + 1, !- Width Factor for Opening Factor 2 + 1, !- Height Factor for Opening Factor 2 + 0, !- Start Height Factor for Opening Factor 2 + 0, !- Opening Factor 3 + 0, !- Discharge Coefficient for Opening Factor 3 + 0, !- Width Factor for Opening Factor 3 + 0, !- Height Factor for Opening Factor 3 + 0, !- Start Height Factor for Opening Factor 3 + 0, !- Opening Factor 4 + 0, !- Discharge Coefficient for Opening Factor 4 + 0, !- Width Factor for Opening Factor 4 + 0, !- Height Factor for Opening Factor 4 + 0; !- Start Height Factor for Opening Factor 4 + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Roof_1_0_0, !- Name + 4.67, !- External Node Height + Block1:Zone2_Roof_1_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Wall_3_0_0, !- Name + 2.33, !- External Node Height + Block1:Zone2_Wall_3_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Name + 2.07, !- External Node Height + Block1:Zone2_Wall_3_0_0_0_0_0_Win; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Wall_4_0_0, !- Name + 2.33, !- External Node Height + Block1:Zone2_Wall_4_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Name + 2.07, !- External Node Height + Block1:Zone2_Wall_4_0_0_0_0_0_Win; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Wall_5_0_0, !- Name + 2.33, !- External Node Height + Block1:Zone2_Wall_5_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Name + 2.07, !- External Node Height + Block1:Zone2_Wall_5_0_0_0_0_0_Win; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Roof_1_0_0, !- Name + 4.67, !- External Node Height + Block1:Zone1_Roof_1_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Wall_2_0_0, !- Name + 2.33, !- External Node Height + Block1:Zone1_Wall_2_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Name + 2.07, !- External Node Height + Block1:Zone1_Wall_2_0_0_0_0_0_Win; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Wall_3_0_0, !- Name + 2.33, !- External Node Height + Block1:Zone1_Wall_3_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Name + 2.07, !- External Node Height + Block1:Zone1_Wall_3_0_0_0_0_0_Win; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Wall_5_0_0, !- Name + 2.33, !- External Node Height + Block1:Zone1_Wall_5_0_0; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:ExternalNode, + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Name + 2.07, !- External Node Height + Block1:Zone1_Wall_5_0_0_0_0_0_Win; !- Wind Pressure Coefficient Curve Name + +AirflowNetwork:MultiZone:WindPressureCoefficientArray, + Cp Data, !- Name + 0, !- Wind Direction 1 + 45, !- Wind Direction 2 + 90, !- Wind Direction 3 + 135, !- Wind Direction 4 + 180, !- Wind Direction 5 + 225, !- Wind Direction 6 + 270, !- Wind Direction 7 + 315; !- Wind Direction 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Roof_1_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.6, !- Wind Pressure Coefficient Value 1 + -0.5, !- Wind Pressure Coefficient Value 2 + -0.4, !- Wind Pressure Coefficient Value 3 + -0.5, !- Wind Pressure Coefficient Value 4 + -0.6, !- Wind Pressure Coefficient Value 5 + -0.5, !- Wind Pressure Coefficient Value 6 + -0.4, !- Wind Pressure Coefficient Value 7 + -0.5; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Wall_3_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + 0.4, !- Wind Pressure Coefficient Value 1 + 0.1, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + -0.35, !- Wind Pressure Coefficient Value 4 + -0.2, !- Wind Pressure Coefficient Value 5 + -0.35, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + 0.1; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + 0.4, !- Wind Pressure Coefficient Value 1 + 0.1, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + -0.35, !- Wind Pressure Coefficient Value 4 + -0.2, !- Wind Pressure Coefficient Value 5 + -0.35, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + 0.1; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Wall_4_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.3, !- Wind Pressure Coefficient Value 1 + -0.35, !- Wind Pressure Coefficient Value 2 + -0.2, !- Wind Pressure Coefficient Value 3 + -0.35, !- Wind Pressure Coefficient Value 4 + -0.3, !- Wind Pressure Coefficient Value 5 + 0.1, !- Wind Pressure Coefficient Value 6 + 0.4, !- Wind Pressure Coefficient Value 7 + 0.1; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.3, !- Wind Pressure Coefficient Value 1 + -0.35, !- Wind Pressure Coefficient Value 2 + -0.2, !- Wind Pressure Coefficient Value 3 + -0.35, !- Wind Pressure Coefficient Value 4 + -0.3, !- Wind Pressure Coefficient Value 5 + 0.1, !- Wind Pressure Coefficient Value 6 + 0.4, !- Wind Pressure Coefficient Value 7 + 0.1; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Wall_5_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.2, !- Wind Pressure Coefficient Value 1 + -0.35, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + 0.1, !- Wind Pressure Coefficient Value 4 + 0.4, !- Wind Pressure Coefficient Value 5 + 0.1, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + -0.35; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.2, !- Wind Pressure Coefficient Value 1 + -0.35, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + 0.1, !- Wind Pressure Coefficient Value 4 + 0.4, !- Wind Pressure Coefficient Value 5 + 0.1, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + -0.35; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Roof_1_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.6, !- Wind Pressure Coefficient Value 1 + -0.5, !- Wind Pressure Coefficient Value 2 + -0.4, !- Wind Pressure Coefficient Value 3 + -0.5, !- Wind Pressure Coefficient Value 4 + -0.6, !- Wind Pressure Coefficient Value 5 + -0.5, !- Wind Pressure Coefficient Value 6 + -0.4, !- Wind Pressure Coefficient Value 7 + -0.5; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Wall_2_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.3, !- Wind Pressure Coefficient Value 1 + 0.1, !- Wind Pressure Coefficient Value 2 + 0.4, !- Wind Pressure Coefficient Value 3 + 0.1, !- Wind Pressure Coefficient Value 4 + -0.3, !- Wind Pressure Coefficient Value 5 + -0.35, !- Wind Pressure Coefficient Value 6 + -0.2, !- Wind Pressure Coefficient Value 7 + -0.35; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.3, !- Wind Pressure Coefficient Value 1 + 0.1, !- Wind Pressure Coefficient Value 2 + 0.4, !- Wind Pressure Coefficient Value 3 + 0.1, !- Wind Pressure Coefficient Value 4 + -0.3, !- Wind Pressure Coefficient Value 5 + -0.35, !- Wind Pressure Coefficient Value 6 + -0.2, !- Wind Pressure Coefficient Value 7 + -0.35; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Wall_3_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + 0.4, !- Wind Pressure Coefficient Value 1 + 0.1, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + -0.35, !- Wind Pressure Coefficient Value 4 + -0.2, !- Wind Pressure Coefficient Value 5 + -0.35, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + 0.1; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + 0.4, !- Wind Pressure Coefficient Value 1 + 0.1, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + -0.35, !- Wind Pressure Coefficient Value 4 + -0.2, !- Wind Pressure Coefficient Value 5 + -0.35, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + 0.1; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Wall_5_0_0, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.2, !- Wind Pressure Coefficient Value 1 + -0.35, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + 0.1, !- Wind Pressure Coefficient Value 4 + 0.4, !- Wind Pressure Coefficient Value 5 + 0.1, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + -0.35; !- Wind Pressure Coefficient Value 8 + +AirflowNetwork:MultiZone:WindPressureCoefficientValues, + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Name + Cp Data, !- AirflowNetworkMultiZoneWindPressureCoefficientArray Name + -0.2, !- Wind Pressure Coefficient Value 1 + -0.35, !- Wind Pressure Coefficient Value 2 + -0.3, !- Wind Pressure Coefficient Value 3 + 0.1, !- Wind Pressure Coefficient Value 4 + 0.4, !- Wind Pressure Coefficient Value 5 + 0.1, !- Wind Pressure Coefficient Value 6 + -0.3, !- Wind Pressure Coefficient Value 7 + -0.35; !- Wind Pressure Coefficient Value 8 + +DESIGNSPECIFICATION:OUTDOORAIR, + BLOCK1:ZONE2 Design Specification Outdoor Air Object, !- Name + Flow/Person, !- Outdoor Air Method + 0.00944, !- Outdoor Air Flow per Person + 0, !- Outdoor Air Flow per Zone Floor Area + 0, !- Outdoor Air Flow per Zone + 0, !- Outdoor Air Flow Air Changes per Hour + On 24/7; !- Outdoor Air Schedule Name + +DESIGNSPECIFICATION:OUTDOORAIR, + BLOCK1:ZONE1 Design Specification Outdoor Air Object, !- Name + Flow/Person, !- Outdoor Air Method + 0.00944, !- Outdoor Air Flow per Person + 0, !- Outdoor Air Flow per Zone Floor Area + 0, !- Outdoor Air Flow per Zone + 0, !- Outdoor Air Flow Air Changes per Hour + On 24/7; !- Outdoor Air Schedule Name + +DESIGNSPECIFICATION:ZONEAIRDISTRIBUTION, + BLOCK1:ZONE2 Design Specification Zone Air Distribution Object, !- Name + 1, !- Zone Air Distribution Effectiveness in Cooling Mode + 1, !- Zone Air Distribution Effectiveness in Heating Mode + , !- Zone Air Distribution Effectiveness Schedule Name + 0, !- Zone Secondary Recirculation Fraction + 0; !- Minimum Zone Ventilation Efficiency + +DESIGNSPECIFICATION:ZONEAIRDISTRIBUTION, + BLOCK1:ZONE1 Design Specification Zone Air Distribution Object, !- Name + 1, !- Zone Air Distribution Effectiveness in Cooling Mode + 1, !- Zone Air Distribution Effectiveness in Heating Mode + , !- Zone Air Distribution Effectiveness Schedule Name + 0, !- Zone Secondary Recirculation Fraction + 0; !- Minimum Zone Ventilation Efficiency + +Sizing:Parameters, + 1.25, !- Heating Sizing Factor + 1.15, !- Cooling Sizing Factor + 2; !- Timesteps in Averaging Window + +SIZING:ZONE, + BLOCK1:ZONE2, !- Zone or ZoneList Name + TemperatureDifference, !- Zone Cooling Design Supply Air Temperature Input Method + 14, !- Zone Cooling Design Supply Air Temperature + 5, !- Zone Cooling Design Supply Air Temperature Difference + TemperatureDifference, !- Zone Heating Design Supply Air Temperature Input Method + 50, !- Zone Heating Design Supply Air Temperature + 5, !- Zone Heating Design Supply Air Temperature Difference + 0.009, !- Zone Cooling Design Supply Air Humidity Ratio + 0.004, !- Zone Heating Design Supply Air Humidity Ratio + BLOCK1:ZONE2 Design Specification Outdoor Air Object, !- Design Specification Outdoor Air Object Name + 1.25, !- Zone Heating Sizing Factor + 1.15, !- Zone Cooling Sizing Factor + DesignDay, !- Cooling Design Air Flow Method + 0, !- Cooling Design Air Flow Rate + 0.00076, !- Cooling Minimum Air Flow per Zone Floor Area + 0, !- Cooling Minimum Air Flow + 0, !- Cooling Minimum Air Flow Fraction + DesignDay, !- Heating Design Air Flow Method + 0, !- Heating Design Air Flow Rate + 0.00203, !- Heating Maximum Air Flow per Zone Floor Area + 0.14158, !- Heating Maximum Air Flow + 0.3, !- Heating Maximum Air Flow Fraction + BLOCK1:ZONE2 Design Specification Zone Air Distribution Object, !- Design Specification Zone Air Distribution Object Name + Yes, !- Account for Dedicated Outdoor Air System + NeutralSupplyAir, !- Dedicated Outdoor Air System Control Strategy + autosize, !- Dedicated Outdoor Air Low Setpoint Temperature for Design + autosize, !- Dedicated Outdoor Air High Setpoint Temperature for Design + Sensible Load Only No Latent Load, !- Zone Load Sizing Method + HumidityRatioDifference, !- Zone Latent Cooling Design Supply Air Humidity Ratio Input Method + , !- Zone Dehumidification Design Supply Air Humidity Ratio + 0.005, !- Zone Cooling Design Supply Air Humidity Ratio Difference + HumidityRatioDifference, !- Zone Latent Heating Design Supply Air Humidity Ratio Input Method + , !- Zone Humidification Design Supply Air Humidity Ratio + 0.005; !- Zone Humidification Design Supply Air Humidity Ratio Difference + +SIZING:ZONE, + BLOCK1:ZONE1, !- Zone or ZoneList Name + TemperatureDifference, !- Zone Cooling Design Supply Air Temperature Input Method + 14, !- Zone Cooling Design Supply Air Temperature + 5, !- Zone Cooling Design Supply Air Temperature Difference + TemperatureDifference, !- Zone Heating Design Supply Air Temperature Input Method + 50, !- Zone Heating Design Supply Air Temperature + 5, !- Zone Heating Design Supply Air Temperature Difference + 0.009, !- Zone Cooling Design Supply Air Humidity Ratio + 0.004, !- Zone Heating Design Supply Air Humidity Ratio + BLOCK1:ZONE1 Design Specification Outdoor Air Object, !- Design Specification Outdoor Air Object Name + 1.25, !- Zone Heating Sizing Factor + 1.15, !- Zone Cooling Sizing Factor + DesignDay, !- Cooling Design Air Flow Method + 0, !- Cooling Design Air Flow Rate + 0.00076, !- Cooling Minimum Air Flow per Zone Floor Area + 0, !- Cooling Minimum Air Flow + 0, !- Cooling Minimum Air Flow Fraction + DesignDay, !- Heating Design Air Flow Method + 0, !- Heating Design Air Flow Rate + 0.00203, !- Heating Maximum Air Flow per Zone Floor Area + 0.14158, !- Heating Maximum Air Flow + 0.3, !- Heating Maximum Air Flow Fraction + BLOCK1:ZONE1 Design Specification Zone Air Distribution Object, !- Design Specification Zone Air Distribution Object Name + Yes, !- Account for Dedicated Outdoor Air System + NeutralSupplyAir, !- Dedicated Outdoor Air System Control Strategy + autosize, !- Dedicated Outdoor Air Low Setpoint Temperature for Design + autosize, !- Dedicated Outdoor Air High Setpoint Temperature for Design + Sensible Load Only No Latent Load, !- Zone Load Sizing Method + HumidityRatioDifference, !- Zone Latent Cooling Design Supply Air Humidity Ratio Input Method + , !- Zone Dehumidification Design Supply Air Humidity Ratio + 0.005, !- Zone Cooling Design Supply Air Humidity Ratio Difference + HumidityRatioDifference, !- Zone Latent Heating Design Supply Air Humidity Ratio Input Method + , !- Zone Humidification Design Supply Air Humidity Ratio + 0.005; !- Zone Humidification Design Supply Air Humidity Ratio Difference + +ZONECONTROL:THERMOSTAT, + BLOCK1:ZONE2 Thermostat, !- Name + BLOCK1:ZONE2, !- Zone or ZoneList Name + Control type schedule: Always 4, !- Control Type Schedule Name + ThermostatSetpoint:DualSetpoint, !- Control 1 Object Type + BLOCK1:ZONE2 Dual SP, !- Control 1 Name + , !- Control 2 Object Type + , !- Control 2 Name + , !- Control 3 Object Type + , !- Control 3 Name + , !- Control 4 Object Type + , !- Control 4 Name + 0; !- Temperature Difference Between Cutout And Setpoint + +ZONECONTROL:THERMOSTAT, + BLOCK1:ZONE1 Thermostat, !- Name + BLOCK1:ZONE1, !- Zone or ZoneList Name + Control type schedule: Always 4, !- Control Type Schedule Name + ThermostatSetpoint:DualSetpoint, !- Control 1 Object Type + BLOCK1:ZONE1 Dual SP, !- Control 1 Name + , !- Control 2 Object Type + , !- Control 2 Name + , !- Control 3 Object Type + , !- Control 3 Name + , !- Control 4 Object Type + , !- Control 4 Name + 0; !- Temperature Difference Between Cutout And Setpoint + +ZONECONTROL:THERMOSTAT:OPERATIVETEMPERATURE, + BLOCK1:ZONE2 Thermostat, !- Thermostat Name + Scheduled, !- Radiative Fraction Input Mode + , !- Fixed Radiative Fraction + TypOperativeTempControlSch, !- Radiative Fraction Schedule Name + None; !- Adaptive Comfort Model Type + +ZONECONTROL:THERMOSTAT:OPERATIVETEMPERATURE, + BLOCK1:ZONE1 Thermostat, !- Thermostat Name + Scheduled, !- Radiative Fraction Input Mode + , !- Fixed Radiative Fraction + TypOperativeTempControlSch, !- Radiative Fraction Schedule Name + None; !- Adaptive Comfort Model Type + +THERMOSTATSETPOINT:DUALSETPOINT, + BLOCK1:ZONE2 Dual SP, !- Name + AHST_Sch_BLOCK1_ZONE2, !- Heating Setpoint Temperature Schedule Name + ACST_Sch_BLOCK1_ZONE2; !- Cooling Setpoint Temperature Schedule Name + +THERMOSTATSETPOINT:DUALSETPOINT, + BLOCK1:ZONE1 Dual SP, !- Name + AHST_Sch_BLOCK1_ZONE1, !- Heating Setpoint Temperature Schedule Name + ACST_Sch_BLOCK1_ZONE1; !- Cooling Setpoint Temperature Schedule Name + +ZoneHVAC:IdealLoadsAirSystem, + Block1:Zone2 Ideal Loads Air, !- Name + , !- Availability Schedule Name + Node Block1:Zone2 In, !- Zone Supply Air Node Name + , !- Zone Exhaust Air Node Name + , !- System Inlet Air Node Name + 35, !- Maximum Heating Supply Air Temperature + 12, !- Minimum Cooling Supply Air Temperature + 0.0156, !- Maximum Heating Supply Air Humidity Ratio + 0.0077, !- Minimum Cooling Supply Air Humidity Ratio + LimitCapacity, !- Heating Limit + Autosize, !- Maximum Heating Air Flow Rate + Autosize, !- Maximum Sensible Heating Capacity + LimitFlowRateAndCapacity, !- Cooling Limit + Autosize, !- Maximum Cooling Air Flow Rate + Autosize, !- Maximum Total Cooling Capacity + On, !- Heating Availability Schedule Name + On, !- Cooling Availability Schedule Name + None, !- Dehumidification Control Type + , !- Cooling Sensible Heat Ratio + None, !- Humidification Control Type + Block1:Zone2, !- Design Specification Outdoor Air Object Name + , !- Outdoor Air Inlet Node Name + , !- Demand Controlled Ventilation Type + NoEconomizer, !- Outdoor Air Economizer Type + None, !- Heat Recovery Type + , !- Sensible Heat Recovery Effectiveness + ; !- Latent Heat Recovery Effectiveness + +ZoneHVAC:IdealLoadsAirSystem, + Block1:Zone1 Ideal Loads Air, !- Name + , !- Availability Schedule Name + Node Block1:Zone1 In, !- Zone Supply Air Node Name + , !- Zone Exhaust Air Node Name + , !- System Inlet Air Node Name + 35, !- Maximum Heating Supply Air Temperature + 12, !- Minimum Cooling Supply Air Temperature + 0.0156, !- Maximum Heating Supply Air Humidity Ratio + 0.0077, !- Minimum Cooling Supply Air Humidity Ratio + LimitCapacity, !- Heating Limit + Autosize, !- Maximum Heating Air Flow Rate + Autosize, !- Maximum Sensible Heating Capacity + LimitFlowRateAndCapacity, !- Cooling Limit + Autosize, !- Maximum Cooling Air Flow Rate + Autosize, !- Maximum Total Cooling Capacity + On, !- Heating Availability Schedule Name + On, !- Cooling Availability Schedule Name + None, !- Dehumidification Control Type + , !- Cooling Sensible Heat Ratio + None, !- Humidification Control Type + Block1:Zone1, !- Design Specification Outdoor Air Object Name + , !- Outdoor Air Inlet Node Name + , !- Demand Controlled Ventilation Type + NoEconomizer, !- Outdoor Air Economizer Type + None, !- Heat Recovery Type + , !- Sensible Heat Recovery Effectiveness + ; !- Latent Heat Recovery Effectiveness + +ZONEHVAC:TERMINALUNIT:VARIABLEREFRIGERANTFLOW, + BLOCK1:ZONE2 VRF Indoor Unit, !- Zone Terminal Unit Name + On 24/7, !- Terminal Unit Availability Schedule + BLOCK1:ZONE2 VRF Indoor Unit Return, !- Terminal Unit Air Inlet Node Name + BLOCK1:ZONE2 VRF Indoor Unit Supply Outlet, !- Terminal Unit Air Outlet Node Name + autosize, !- Cooling Supply Air Flow Rate + autosize, !- No Cooling Supply Air Flow Rate + autosize, !- Heating Supply Air Flow Rate + autosize, !- No Heating Supply Air Flow Rate + 0, !- Cooling Outdoor Air Flow Rate + 0, !- Heating Outdoor Air Flow Rate + 0, !- No Load Outdoor Air Flow Rate + On 24/7, !- Supply Air Fan Operating Mode Schedule Name + DrawThrough, !- Supply Air Fan Placement + Fan:ConstantVolume, !- Supply Air Fan Object Type + BLOCK1:ZONE2 VRF Indoor Unit Supply Fan, !- Supply Air Fan Object Name + , !- Outside Air Mixer Object Type + , !- Outside Air Mixer Object Name + Coil:Cooling:DX:VariableRefrigerantFlow, !- Cooling Coil Object Type + BLOCK1:ZONE2 VRF Indoor Unit DX Cooling Coil, !- Cooling Coil Object Name + Coil:Heating:DX:VariableRefrigerantFlow, !- Heating Coil Object Type + BLOCK1:ZONE2 VRF Indoor Unit DX Heating Coil, !- Heating Coil Object Name + 30, !- Zone Terminal Unit On Parasitic Electric Energy Use + 20, !- Zone Terminal Unit Off Parasitic Electric Energy Use + , !- Rated Heating Capacity Sizing Ratio + , !- Availability Manager List Name + , !- Design Specification ZoneHVAC Sizing Object Name + , !- Supplemental Heating Coil Object Type + , !- Supplemental Heating Coil Name + autosize, !- Maximum Supply Air Temperature from Supplemental Heater + 21; !- Maximum Outdoor DryBulb Temperature for Supplemental Heater Operation + +ZONEHVAC:TERMINALUNIT:VARIABLEREFRIGERANTFLOW, + BLOCK1:ZONE1 VRF Indoor Unit, !- Zone Terminal Unit Name + On 24/7, !- Terminal Unit Availability Schedule + BLOCK1:ZONE1 VRF Indoor Unit Return, !- Terminal Unit Air Inlet Node Name + BLOCK1:ZONE1 VRF Indoor Unit Supply Outlet, !- Terminal Unit Air Outlet Node Name + autosize, !- Cooling Supply Air Flow Rate + autosize, !- No Cooling Supply Air Flow Rate + autosize, !- Heating Supply Air Flow Rate + autosize, !- No Heating Supply Air Flow Rate + 0, !- Cooling Outdoor Air Flow Rate + 0, !- Heating Outdoor Air Flow Rate + 0, !- No Load Outdoor Air Flow Rate + On 24/7, !- Supply Air Fan Operating Mode Schedule Name + DrawThrough, !- Supply Air Fan Placement + Fan:ConstantVolume, !- Supply Air Fan Object Type + BLOCK1:ZONE1 VRF Indoor Unit Supply Fan, !- Supply Air Fan Object Name + , !- Outside Air Mixer Object Type + , !- Outside Air Mixer Object Name + Coil:Cooling:DX:VariableRefrigerantFlow, !- Cooling Coil Object Type + BLOCK1:ZONE1 VRF Indoor Unit DX Cooling Coil, !- Cooling Coil Object Name + Coil:Heating:DX:VariableRefrigerantFlow, !- Heating Coil Object Type + BLOCK1:ZONE1 VRF Indoor Unit DX Heating Coil, !- Heating Coil Object Name + 30, !- Zone Terminal Unit On Parasitic Electric Energy Use + 20, !- Zone Terminal Unit Off Parasitic Electric Energy Use + , !- Rated Heating Capacity Sizing Ratio + , !- Availability Manager List Name + , !- Design Specification ZoneHVAC Sizing Object Name + , !- Supplemental Heating Coil Object Type + , !- Supplemental Heating Coil Name + autosize, !- Maximum Supply Air Temperature from Supplemental Heater + 21; !- Maximum Outdoor DryBulb Temperature for Supplemental Heater Operation + +ZONEHVAC:EQUIPMENTLIST, + BLOCK1:ZONE2 Equipment, !- Name + SequentialLoad, !- Load Distribution Scheme + ZoneHVAC:TerminalUnit:VariableRefrigerantFlow, !- Zone Equipment 1 Object Type + BLOCK1:ZONE2 VRF Indoor Unit, !- Zone Equipment 1 Name + 1, !- Zone Equipment 1 Cooling Sequence + 1, !- Zone Equipment 1 Heating or NoLoad Sequence + , !- Zone Equipment 1 Sequential Cooling Fraction Schedule Name + ; !- Zone Equipment 1 Sequential Heating Fraction Schedule Name + +ZONEHVAC:EQUIPMENTLIST, + BLOCK1:ZONE1 Equipment, !- Name + SequentialLoad, !- Load Distribution Scheme + ZoneHVAC:TerminalUnit:VariableRefrigerantFlow, !- Zone Equipment 1 Object Type + BLOCK1:ZONE1 VRF Indoor Unit, !- Zone Equipment 1 Name + 1, !- Zone Equipment 1 Cooling Sequence + 1, !- Zone Equipment 1 Heating or NoLoad Sequence + , !- Zone Equipment 1 Sequential Cooling Fraction Schedule Name + ; !- Zone Equipment 1 Sequential Heating Fraction Schedule Name + +ZONEHVAC:EQUIPMENTCONNECTIONS, + BLOCK1:ZONE2, !- Zone Name + BLOCK1:ZONE2 Equipment, !- Zone Conditioning Equipment List Name + BLOCK1:ZONE2 Air Inlet Node List, !- Zone Air Inlet Node or NodeList Name + BLOCK1:ZONE2 Air Exhaust Node List, !- Zone Air Exhaust Node or NodeList Name + BLOCK1:ZONE2 Zone Air Node, !- Zone Air Node Name + BLOCK1:ZONE2 Return Outlet; !- Zone Return Air Node or NodeList Name + +ZONEHVAC:EQUIPMENTCONNECTIONS, + BLOCK1:ZONE1, !- Zone Name + BLOCK1:ZONE1 Equipment, !- Zone Conditioning Equipment List Name + BLOCK1:ZONE1 Air Inlet Node List, !- Zone Air Inlet Node or NodeList Name + BLOCK1:ZONE1 Air Exhaust Node List, !- Zone Air Exhaust Node or NodeList Name + BLOCK1:ZONE1 Zone Air Node, !- Zone Air Node Name + BLOCK1:ZONE1 Return Outlet; !- Zone Return Air Node or NodeList Name + +FAN:CONSTANTVOLUME, + BLOCK1:ZONE2 VRF Indoor Unit Supply Fan, !- Name + On 24/7, !- Availability Schedule Name + 0.7, !- Fan Total Efficiency + 100, !- Pressure Rise + autosize, !- Maximum Flow Rate + 0.9, !- Motor Efficiency + 1, !- Motor In Airstream Fraction + BLOCK1:ZONE2 VRF Indoor Unit DX Heating Coil Outlet, !- Air Inlet Node Name + BLOCK1:ZONE2 VRF Indoor Unit Supply Outlet, !- Air Outlet Node Name + General; !- EndUse Subcategory + +FAN:CONSTANTVOLUME, + BLOCK1:ZONE1 VRF Indoor Unit Supply Fan, !- Name + On 24/7, !- Availability Schedule Name + 0.7, !- Fan Total Efficiency + 100, !- Pressure Rise + autosize, !- Maximum Flow Rate + 0.9, !- Motor Efficiency + 1, !- Motor In Airstream Fraction + BLOCK1:ZONE1 VRF Indoor Unit DX Heating Coil Outlet, !- Air Inlet Node Name + BLOCK1:ZONE1 VRF Indoor Unit Supply Outlet, !- Air Outlet Node Name + General; !- EndUse Subcategory + +COIL:COOLING:DX:VARIABLEREFRIGERANTFLOW, + BLOCK1:ZONE2 VRF Indoor Unit DX Cooling Coil, !- Name + On 24/7, !- Availability Schedule Name + autosize, !- Gross Rated Total Cooling Capacity + autosize, !- Gross Rated Sensible Heat Ratio + autosize, !- Rated Air Flow Rate + VRFTUCoolCapFT, !- Cooling Capacity Ratio Modifier Function of Temperature Curve Name + VRFACCoolCapFFF, !- Cooling Capacity Modifier Curve Function of Flow Fraction Name + BLOCK1:ZONE2 VRF Indoor Unit Return, !- Coil Air Inlet Node + BLOCK1:ZONE2 VRF Indoor Unit DX Cooling Coil Outlet, !- Coil Air Outlet Node + ; !- Name of Water Storage Tank for Condensate Collection + +COIL:COOLING:DX:VARIABLEREFRIGERANTFLOW, + BLOCK1:ZONE1 VRF Indoor Unit DX Cooling Coil, !- Name + On 24/7, !- Availability Schedule Name + autosize, !- Gross Rated Total Cooling Capacity + autosize, !- Gross Rated Sensible Heat Ratio + autosize, !- Rated Air Flow Rate + VRFTUCoolCapFT, !- Cooling Capacity Ratio Modifier Function of Temperature Curve Name + VRFACCoolCapFFF, !- Cooling Capacity Modifier Curve Function of Flow Fraction Name + BLOCK1:ZONE1 VRF Indoor Unit Return, !- Coil Air Inlet Node + BLOCK1:ZONE1 VRF Indoor Unit DX Cooling Coil Outlet, !- Coil Air Outlet Node + ; !- Name of Water Storage Tank for Condensate Collection + +COIL:HEATING:DX:VARIABLEREFRIGERANTFLOW, + BLOCK1:ZONE2 VRF Indoor Unit DX Heating Coil, !- Name + On 24/7, !- Availability Schedule + autosize, !- Gross Rated Heating Capacity + autosize, !- Rated Air Flow Rate + BLOCK1:ZONE2 VRF Indoor Unit DX Cooling Coil Outlet, !- Coil Air Inlet Node + BLOCK1:ZONE2 VRF Indoor Unit DX Heating Coil Outlet, !- Coil Air Outlet Node + VRFTUHeatCapFT, !- Heating Capacity Ratio Modifier Function of Temperature Curve Name + VRFACCoolCapFFF; !- Heating Capacity Modifier Function of Flow Fraction Curve Name + +COIL:HEATING:DX:VARIABLEREFRIGERANTFLOW, + BLOCK1:ZONE1 VRF Indoor Unit DX Heating Coil, !- Name + On 24/7, !- Availability Schedule + autosize, !- Gross Rated Heating Capacity + autosize, !- Rated Air Flow Rate + BLOCK1:ZONE1 VRF Indoor Unit DX Cooling Coil Outlet, !- Coil Air Inlet Node + BLOCK1:ZONE1 VRF Indoor Unit DX Heating Coil Outlet, !- Coil Air Outlet Node + VRFTUHeatCapFT, !- Heating Capacity Ratio Modifier Function of Temperature Curve Name + VRFACCoolCapFFF; !- Heating Capacity Modifier Function of Flow Fraction Curve Name + +AIRCONDITIONER:VARIABLEREFRIGERANTFLOW, + VRF Outdoor Unit_BLOCK1:ZONE2, !- Heat Pump Name + On 24/7, !- Availability Schedule Name + autosize, !- Gross Rated Total Cooling Capacity + 2, !- Gross Rated Cooling COP + -20, !- Minimum Condenser Inlet Node Temperature in Cooling Mode + 80, !- Maximum Condenser Inlet Node Temperature in Cooling Mode + VRFCoolCapFT, !- Cooling Capacity Ratio Modifier Function of Low Temperature Curve Name + VRFCoolCapFTBoundary, !- Cooling Capacity Ratio Boundary Curve Name + VRFCoolCapFTHi, !- Cooling Capacity Ratio Modifier Function of High Temperature Curve Name + VRFCoolEIRFT, !- Cooling Energy Input Ratio Modifier Function of Low Temperature Curve Name + VRFCoolEIRFTBoundary, !- Cooling Energy Input Ratio Boundary Curve Name + VRFCoolEIRFTHi, !- Cooling Energy Input Ratio Modifier Function of High Temperature Curve Name + CoolingEIRLowPLR, !- Cooling Energy Input Ratio Modifier Function of Low PartLoad Ratio Curve Name + CoolingEIRHiPLR, !- Cooling Energy Input Ratio Modifier Function of High PartLoad Ratio Curve Name + CoolingCombRatio, !- Cooling Combination Ratio Correction Factor Curve Name + VRFCPLFFPLR, !- Cooling PartLoad Fraction Correlation Curve Name + autosize, !- Gross Rated Heating Capacity + 1, !- Rated Heating Capacity Sizing Ratio + 2.1, !- Gross Rated Heating COP + -20, !- Minimum Condenser Inlet Node Temperature in Heating Mode + 80, !- Maximum Condenser Inlet Node Temperature in Heating Mode + VRFHeatCapFT, !- Heating Capacity Ratio Modifier Function of Low Temperature Curve Name + VRFHeatCapFTBoundary, !- Heating Capacity Ratio Boundary Curve Name + VRFHeatCapFTHi, !- Heating Capacity Ratio Modifier Function of High Temperature Curve Name + VRFHeatEIRFT, !- Heating Energy Input Ratio Modifier Function of Low Temperature Curve Name + VRFHeatEIRFTBoundary, !- Heating Energy Input Ratio Boundary Curve Name + VRFHeatEIRFTHi, !- Heating Energy Input Ratio Modifier Function of High Temperature Curve Name + WetBulbTemperature, !- Heating Performance Curve Outdoor Temperature Type + HeatingEIRLowPLR, !- Heating Energy Input Ratio Modifier Function of Low PartLoad Ratio Curve Name + HeatingEIRHiPLR, !- Heating Energy Input Ratio Modifier Function of High PartLoad Ratio Curve Name + HeatingCombRatio, !- Heating Combination Ratio Correction Factor Curve Name + VRFCPLFFPLR, !- Heating PartLoad Fraction Correlation Curve Name + 0.2, !- Minimum Heat Pump PartLoad Ratio + , !- Zone Name for Master Thermostat Location + LoadPriority, !- Master Thermostat Priority Control Type + , !- Thermostat Priority Schedule Name + VRF Outdoor Unit_BLOCK1:ZONE2 Zone List, !- Zone Terminal Unit List Name + Yes, !- Heat Pump Waste Heat Recovery + 50, !- Equivalent Piping Length used for Piping Correction Factor in Cooling Mode + 15, !- Vertical Height used for Piping Correction Factor + CoolingLengthCorrectionFactor, !- Piping Correction Factor for Length in Cooling Mode Curve Name + 0, !- Piping Correction Factor for Height in Cooling Mode Coefficient + 50, !- Equivalent Piping Length used for Piping Correction Factor in Heating Mode + VRF Piping Correction Factor for Length in Heating Mode, !- Piping Correction Factor for Length in Heating Mode Curve Name + 0, !- Piping Correction Factor for Height in Heating Mode Coefficient + 15, !- Crankcase Heater Power per Compressor + 2, !- Number of Compressors + 0.5, !- Ratio of Compressor Size to Total Compressor Capacity + 5, !- Maximum Outdoor DryBulb Temperature for Crankcase Heater + Resistive, !- Defrost Strategy + Timed, !- Defrost Control + , !- Defrost Energy Input Ratio Modifier Function of Temperature Curve Name + 0, !- Defrost Time Period Fraction + autosize, !- Resistive Defrost Heater Capacity + 5, !- Maximum Outdoor Drybulb Temperature for Defrost Operation + AirCooled, !- Condenser Type + VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node, !- Condenser Inlet Node Name + , !- Condenser Outlet Node Name + autosize, !- Water Condenser Volume Flow Rate + 0.9, !- Evaporative Condenser Effectiveness + autosize, !- Evaporative Condenser Air Flow Rate + autosize, !- Evaporative Condenser Pump Rated Power Consumption + , !- Supply Water Storage Tank Name + 0, !- Basin Heater Capacity + 2, !- Basin Heater Setpoint Temperature + On 24/7, !- Basin Heater Operating Schedule Name + Electricity, !- Fuel Type + -5.99, !- Minimum Condenser Inlet Node Temperature in Heat Recovery Mode + 80, !- Maximum Condenser Inlet Node Temperature in Heat Recovery Mode + VRF Heat Recovery Cooling Capacity Modifier, !- Heat Recovery Cooling Capacity Modifier Curve Name + 0.5, !- Initial Heat Recovery Cooling Capacity Fraction + 0.15, !- Heat Recovery Cooling Capacity Time Constant + VRF Heat Recovery Cooling Energy Modifier, !- Heat Recovery Cooling Energy Modifier Curve Name + 1, !- Initial Heat Recovery Cooling Energy Fraction + 0, !- Heat Recovery Cooling Energy Time Constant + VRF Heat Recovery Heating Capacity Modifier, !- Heat Recovery Heating Capacity Modifier Curve Name + 1, !- Initial Heat Recovery Heating Capacity Fraction + 0.15, !- Heat Recovery Heating Capacity Time Constant + VRF Heat Recovery Heating Energy Modifier, !- Heat Recovery Heating Energy Modifier Curve Name + 1, !- Initial Heat Recovery Heating Energy Fraction + 0; !- Heat Recovery Heating Energy Time Constant + +AIRCONDITIONER:VARIABLEREFRIGERANTFLOW, + VRF Outdoor Unit_BLOCK1:ZONE1, !- Heat Pump Name + On 24/7, !- Availability Schedule Name + autosize, !- Gross Rated Total Cooling Capacity + 2, !- Gross Rated Cooling COP + -20, !- Minimum Condenser Inlet Node Temperature in Cooling Mode + 80, !- Maximum Condenser Inlet Node Temperature in Cooling Mode + VRFCoolCapFT, !- Cooling Capacity Ratio Modifier Function of Low Temperature Curve Name + VRFCoolCapFTBoundary, !- Cooling Capacity Ratio Boundary Curve Name + VRFCoolCapFTHi, !- Cooling Capacity Ratio Modifier Function of High Temperature Curve Name + VRFCoolEIRFT, !- Cooling Energy Input Ratio Modifier Function of Low Temperature Curve Name + VRFCoolEIRFTBoundary, !- Cooling Energy Input Ratio Boundary Curve Name + VRFCoolEIRFTHi, !- Cooling Energy Input Ratio Modifier Function of High Temperature Curve Name + CoolingEIRLowPLR, !- Cooling Energy Input Ratio Modifier Function of Low PartLoad Ratio Curve Name + CoolingEIRHiPLR, !- Cooling Energy Input Ratio Modifier Function of High PartLoad Ratio Curve Name + CoolingCombRatio, !- Cooling Combination Ratio Correction Factor Curve Name + VRFCPLFFPLR, !- Cooling PartLoad Fraction Correlation Curve Name + autosize, !- Gross Rated Heating Capacity + 1, !- Rated Heating Capacity Sizing Ratio + 2.1, !- Gross Rated Heating COP + -20, !- Minimum Condenser Inlet Node Temperature in Heating Mode + 80, !- Maximum Condenser Inlet Node Temperature in Heating Mode + VRFHeatCapFT, !- Heating Capacity Ratio Modifier Function of Low Temperature Curve Name + VRFHeatCapFTBoundary, !- Heating Capacity Ratio Boundary Curve Name + VRFHeatCapFTHi, !- Heating Capacity Ratio Modifier Function of High Temperature Curve Name + VRFHeatEIRFT, !- Heating Energy Input Ratio Modifier Function of Low Temperature Curve Name + VRFHeatEIRFTBoundary, !- Heating Energy Input Ratio Boundary Curve Name + VRFHeatEIRFTHi, !- Heating Energy Input Ratio Modifier Function of High Temperature Curve Name + WetBulbTemperature, !- Heating Performance Curve Outdoor Temperature Type + HeatingEIRLowPLR, !- Heating Energy Input Ratio Modifier Function of Low PartLoad Ratio Curve Name + HeatingEIRHiPLR, !- Heating Energy Input Ratio Modifier Function of High PartLoad Ratio Curve Name + HeatingCombRatio, !- Heating Combination Ratio Correction Factor Curve Name + VRFCPLFFPLR, !- Heating PartLoad Fraction Correlation Curve Name + 0.2, !- Minimum Heat Pump PartLoad Ratio + , !- Zone Name for Master Thermostat Location + LoadPriority, !- Master Thermostat Priority Control Type + , !- Thermostat Priority Schedule Name + VRF Outdoor Unit_BLOCK1:ZONE1 Zone List, !- Zone Terminal Unit List Name + Yes, !- Heat Pump Waste Heat Recovery + 50, !- Equivalent Piping Length used for Piping Correction Factor in Cooling Mode + 15, !- Vertical Height used for Piping Correction Factor + CoolingLengthCorrectionFactor, !- Piping Correction Factor for Length in Cooling Mode Curve Name + 0, !- Piping Correction Factor for Height in Cooling Mode Coefficient + 50, !- Equivalent Piping Length used for Piping Correction Factor in Heating Mode + VRF Piping Correction Factor for Length in Heating Mode, !- Piping Correction Factor for Length in Heating Mode Curve Name + 0, !- Piping Correction Factor for Height in Heating Mode Coefficient + 15, !- Crankcase Heater Power per Compressor + 2, !- Number of Compressors + 0.5, !- Ratio of Compressor Size to Total Compressor Capacity + 5, !- Maximum Outdoor DryBulb Temperature for Crankcase Heater + Resistive, !- Defrost Strategy + Timed, !- Defrost Control + , !- Defrost Energy Input Ratio Modifier Function of Temperature Curve Name + 0, !- Defrost Time Period Fraction + autosize, !- Resistive Defrost Heater Capacity + 5, !- Maximum Outdoor Drybulb Temperature for Defrost Operation + AirCooled, !- Condenser Type + VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node, !- Condenser Inlet Node Name + , !- Condenser Outlet Node Name + autosize, !- Water Condenser Volume Flow Rate + 0.9, !- Evaporative Condenser Effectiveness + autosize, !- Evaporative Condenser Air Flow Rate + autosize, !- Evaporative Condenser Pump Rated Power Consumption + , !- Supply Water Storage Tank Name + 0, !- Basin Heater Capacity + 2, !- Basin Heater Setpoint Temperature + On 24/7, !- Basin Heater Operating Schedule Name + Electricity, !- Fuel Type + -5.99, !- Minimum Condenser Inlet Node Temperature in Heat Recovery Mode + 80, !- Maximum Condenser Inlet Node Temperature in Heat Recovery Mode + VRF Heat Recovery Cooling Capacity Modifier, !- Heat Recovery Cooling Capacity Modifier Curve Name + 0.5, !- Initial Heat Recovery Cooling Capacity Fraction + 0.15, !- Heat Recovery Cooling Capacity Time Constant + VRF Heat Recovery Cooling Energy Modifier, !- Heat Recovery Cooling Energy Modifier Curve Name + 1, !- Initial Heat Recovery Cooling Energy Fraction + 0, !- Heat Recovery Cooling Energy Time Constant + VRF Heat Recovery Heating Capacity Modifier, !- Heat Recovery Heating Capacity Modifier Curve Name + 1, !- Initial Heat Recovery Heating Capacity Fraction + 0.15, !- Heat Recovery Heating Capacity Time Constant + VRF Heat Recovery Heating Energy Modifier, !- Heat Recovery Heating Energy Modifier Curve Name + 1, !- Initial Heat Recovery Heating Energy Fraction + 0; !- Heat Recovery Heating Energy Time Constant + +ZONETERMINALUNITLIST, + VRF Outdoor Unit_BLOCK1:ZONE2 Zone List, !- Zone Terminal Unit List Name + BLOCK1:ZONE2 VRF Indoor Unit; !- Zone Terminal Unit Name 1 + +ZONETERMINALUNITLIST, + VRF Outdoor Unit_BLOCK1:ZONE1 Zone List, !- Zone Terminal Unit List Name + BLOCK1:ZONE1 VRF Indoor Unit; !- Zone Terminal Unit Name 1 + +NODELIST, + BLOCK1:ZONE2 Air Inlet Node List, !- Name + BLOCK1:ZONE2 VRF Indoor Unit Supply Outlet; !- Node 1 Name + +NODELIST, + BLOCK1:ZONE2 Air Exhaust Node List, !- Name + BLOCK1:ZONE2 VRF Indoor Unit Return; !- Node 1 Name + +NODELIST, + BLOCK1:ZONE1 Air Inlet Node List, !- Name + BLOCK1:ZONE1 VRF Indoor Unit Supply Outlet; !- Node 1 Name + +NODELIST, + BLOCK1:ZONE1 Air Exhaust Node List, !- Name + BLOCK1:ZONE1 VRF Indoor Unit Return; !- Node 1 Name + +OUTDOORAIR:NODELIST, + VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node; !- Node or NodeList Name 1 + +OUTDOORAIR:NODELIST, + VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node; !- Node or NodeList Name 1 + +ENERGYMANAGEMENTSYSTEM:SENSOR, + RMOT, !- Name + People Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + PMOT, !- Name + People Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Occ_count_BLOCK1_ZONE2, !- Name + People BLOCK1:ZONE2, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Occ_count_BLOCK1_ZONE1, !- Name + People BLOCK1:ZONE1, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE2_OpT, !- Name + BLOCK1:ZONE2, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE2_WindSpeed, !- Name + BLOCK1:ZONE2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE2_OutT, !- Name + BLOCK1:ZONE2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE1_OpT, !- Name + BLOCK1:ZONE1, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE1_WindSpeed, !- Name + BLOCK1:ZONE1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE1_OutT, !- Name + BLOCK1:ZONE1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count, !- Name + People Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count, !- Name + People Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT, !- Name + Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count, !- Name + People Block1:Zone2, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count, !- Name + People Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count, !- Name + People Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Operative Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Wind Speed; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT, !- Name + Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + Zone Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count, !- Name + People Block1:Zone1, !- OutputVariable or OutputMeter Index Key Name + People Occupant Count; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + OutT, !- Name + Environment, !- OutputVariable or OutputMeter Index Key Name + Site Outdoor Air Drybulb Temperature; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE2_CoolCoil, !- Name + BLOCK1:ZONE2 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE2_HeatCoil, !- Name + BLOCK1:ZONE2 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE1_CoolCoil, !- Name + BLOCK1:ZONE1 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + BLOCK1_ZONE1_HeatCoil, !- Name + BLOCK1:ZONE1 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil, !- Name + Block1:Zone2 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil, !- Name + Block1:Zone2 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil, !- Name + Block1:Zone2 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil, !- Name + Block1:Zone2 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil, !- Name + Block1:Zone2 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil, !- Name + Block1:Zone2 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil, !- Name + Block1:Zone1 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil, !- Name + Block1:Zone1 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil, !- Name + Block1:Zone1 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil, !- Name + Block1:Zone1 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil, !- Name + Block1:Zone1 VRF Indoor Unit DX Cooling Coil, !- OutputVariable or OutputMeter Index Key Name + Cooling Coil Total Cooling Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:SENSOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil, !- Name + Block1:Zone1 VRF Indoor Unit DX Heating Coil, !- OutputVariable or OutputMeter Index Key Name + Heating Coil Heating Rate; !- OutputVariable or OutputMeter Name + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + AHST_Act_BLOCK1_ZONE2, !- Name + AHST_Sch_BLOCK1_ZONE2, !- Actuated Component Unique Name + Schedule:Compact, !- Actuated Component Type + Schedule Value; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + ACST_Act_BLOCK1_ZONE2, !- Name + ACST_Sch_BLOCK1_ZONE2, !- Actuated Component Unique Name + Schedule:Compact, !- Actuated Component Type + Schedule Value; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + AHST_Act_BLOCK1_ZONE1, !- Name + AHST_Sch_BLOCK1_ZONE1, !- Actuated Component Unique Name + Schedule:Compact, !- Actuated Component Type + Schedule Value; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + ACST_Act_BLOCK1_ZONE1, !- Name + ACST_Sch_BLOCK1_ZONE1, !- Actuated Component Unique Name + Schedule:Compact, !- Actuated Component Type + Schedule Value; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact, !- Name + Block1:Zone2_Wall_3_0_0_0_0_0_Win, !- Actuated Component Unique Name + AirFlow Network Window/Door Opening, !- Actuated Component Type + Venting Opening Factor; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact, !- Name + Block1:Zone2_Wall_4_0_0_0_0_0_Win, !- Actuated Component Unique Name + AirFlow Network Window/Door Opening, !- Actuated Component Type + Venting Opening Factor; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact, !- Name + Block1:Zone2_Wall_5_0_0_0_0_0_Win, !- Actuated Component Unique Name + AirFlow Network Window/Door Opening, !- Actuated Component Type + Venting Opening Factor; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact, !- Name + Block1:Zone1_Wall_2_0_0_0_0_0_Win, !- Actuated Component Unique Name + AirFlow Network Window/Door Opening, !- Actuated Component Type + Venting Opening Factor; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact, !- Name + Block1:Zone1_Wall_3_0_0_0_0_0_Win, !- Actuated Component Unique Name + AirFlow Network Window/Door Opening, !- Actuated Component Type + Venting Opening Factor; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:ACTUATOR, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact, !- Name + Block1:Zone1_Wall_5_0_0_0_0_0_Win, !- Actuated Component Unique Name + AirFlow Network Window/Door Opening, !- Actuated Component Type + Venting Opening Factor; !- Actuated Component Control Type + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetComfTemp, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetComfTemp; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + CountHours_BLOCK1_ZONE2, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + CountHours_BLOCK1_ZONE2; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + CountHours_BLOCK1_ZONE1, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + CountHours_BLOCK1_ZONE1; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetAppLimits, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetAppLimits; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + ApplyCAT, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + ApplyCAT; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetAST, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetAST; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetASTnoTol, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetASTnoTol; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + CountHoursNoApp_BLOCK1_ZONE2, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + CountHoursNoApp_BLOCK1_ZONE2; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetGeoVarBLOCK1_ZONE2, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetGeoVarBLOCK1_ZONE2; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + CountHoursNoApp_BLOCK1_ZONE1, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + CountHoursNoApp_BLOCK1_ZONE1; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetGeoVarBLOCK1_ZONE1, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetGeoVarBLOCK1_ZONE1; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetInputData, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetInputData; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetVOFinputData, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetVOFinputData; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetVST, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetVST; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + ApplyAST_BLOCK1_ZONE2, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + ApplyAST_BLOCK1_ZONE2; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + ApplyAST_BLOCK1_ZONE1, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + ApplyAST_BLOCK1_ZONE1; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAMCALLINGMANAGER, + SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win, !- Name + BeginTimestepBeforePredictor, !- EnergyPlus Model Calling Point + SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win; !- Program Name 1 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetComfTemp, !- Name + if ComfStand == 99, !- Program Line 1 + set ComfTemp = PMOT*m+n, !- Program Line 2 + elseif ComfStand == 1, !- Program Line 3 + set ComfTemp = RMOT*0.33+18.8, !- Program Line 4 + elseif ComfStand == 2, !- Program Line 5 + set ComfTemp = PMOT*0.31+17.8, !- Program Line 6 + elseif ComfStand == 3, !- Program Line 7 + set ComfTemp = PMOT*0.48+14.4, !- Program Line 8 + elseif ComfStand == 4, !- Program Line 9 + set ComfTemp = 0, !- Program Line 10 + elseif ComfStand == 5, !- Program Line 11 + set ComfTemp = 0, !- Program Line 12 + elseif ComfStand == 6, !- Program Line 13 + set ComfTemp = 0, !- Program Line 14 + elseif ComfStand == 7, !- Program Line 15 + set ComfTemp = PMOT*0.54+12.83, !- Program Line 16 + elseif ComfStand == 8, !- Program Line 17 + set ComfTemp = PMOT*0.28+17.87, !- Program Line 18 + elseif ComfStand == 9, !- Program Line 19 + set ComfTemp = PMOT*0.39+18.42, !- Program Line 20 + elseif ComfStand == 10, !- Program Line 21 + set ComfTemp = PMOT*0.42+17.6, !- Program Line 22 + elseif ComfStand == 11, !- Program Line 23 + set ComfTemp = PMOT*0.75+5.37, !- Program Line 24 + elseif ComfStand == 12, !- Program Line 25 + set ComfTemp = PMOT*0.25+19.7, !- Program Line 26 + elseif ComfStand == 13, !- Program Line 27 + set ComfTemp = PMOT*0.26+15.9, !- Program Line 28 + elseif ComfStand == 14, !- Program Line 29 + set ComfTemp = PMOT*0.26+16.75, !- Program Line 30 + elseif ComfStand == 15, !- Program Line 31 + set ComfTemp = PMOT*0.56+12.74, !- Program Line 32 + elseif ComfStand == 16, !- Program Line 33 + set ComfTemp = PMOT*0.09+22.32, !- Program Line 34 + elseif ComfStand == 17, !- Program Line 35 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 36 + set ComfTemp = PMOT*0.48+13.9, !- Program Line 37 + else, !- Program Line 38 + set ComfTemp = PMOT*0.59+9.6, !- Program Line 39 + endif, !- Program Line 40 + elseif ComfStand == 18, !- Program Line 41 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 42 + set ComfTemp = PMOT*0.84+5.3, !- Program Line 43 + else, !- Program Line 44 + set ComfTemp = PMOT*0.96-3.6, !- Program Line 45 + endif, !- Program Line 46 + elseif ComfStand == 19, !- Program Line 47 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 48 + set ComfTemp = PMOT*0.27+17.9, !- Program Line 49 + else, !- Program Line 50 + set ComfTemp = PMOT*0.53+10.3, !- Program Line 51 + endif, !- Program Line 52 + elseif ComfStand == 20, !- Program Line 53 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 54 + set ComfTemp = PMOT*0.38+15.7, !- Program Line 55 + else, !- Program Line 56 + set ComfTemp = PMOT*0.47+9.07, !- Program Line 57 + endif, !- Program Line 58 + elseif ComfStand == 21, !- Program Line 59 + set ComfTemp = PMOT*0.678+13.51, !- Program Line 60 + endif; !- Program Line 61 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + CountHours_BLOCK1_ZONE2, !- Name + if (ComfStand == 1) || (ComfStand == 10), !- Program Line 1 + if (RMOT >= AHSTall) && (RMOT <= ACSTaul), !- Program Line 2 + if (BLOCK1_ZONE2_OpT <= ACSTnoTol), !- Program Line 3 + if (BLOCK1_ZONE2_OpT >= AHSTnoTol), !- Program Line 4 + set ComfHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 5 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 6 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 7 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 8 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 9 + endif, !- Program Line 10 + elseif (BLOCK1_ZONE2_OpT > ACSTnoTol), !- Program Line 11 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 12 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 13 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 14 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 15 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 16 + elseif (BLOCK1_ZONE2_OpT < AHSTnoTol), !- Program Line 17 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 18 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 19 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 20 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 21 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 22 + endif, !- Program Line 23 + elseif (RMOT > ACSTaul), !- Program Line 24 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 25 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 26 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 27 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 28 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 29 + elseif (RMOT < AHSTall), !- Program Line 30 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 31 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 32 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 33 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 34 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + if (PMOT >= AHSTall) && (PMOT <= ACSTaul), !- Program Line 38 + if (BLOCK1_ZONE2_OpT <= ACSTnoTol), !- Program Line 39 + if (BLOCK1_ZONE2_OpT >= AHSTnoTol), !- Program Line 40 + set ComfHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 41 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 42 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 43 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 44 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 45 + endif, !- Program Line 46 + elseif (BLOCK1_ZONE2_OpT > ACSTnoTol), !- Program Line 47 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 48 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 49 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 50 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 51 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 52 + elseif (BLOCK1_ZONE2_OpT < AHSTnoTol), !- Program Line 53 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 54 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 55 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 56 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 57 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 58 + endif, !- Program Line 59 + elseif (PMOT > ACSTaul), !- Program Line 60 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 61 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 62 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 63 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 64 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 65 + elseif (PMOT < AHSTall), !- Program Line 66 + set ComfHours_BLOCK1_ZONE2 = 0, !- Program Line 67 + set DiscomfAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 68 + set DiscomfAppColdHours_BLOCK1_ZONE2 = 0, !- Program Line 69 + set DiscomfNonAppHotHours_BLOCK1_ZONE2 = 0, !- Program Line 70 + set DiscomfNonAppColdHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 71 + endif, !- Program Line 72 + endif; !- Program Line 73 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + CountHours_BLOCK1_ZONE1, !- Name + if (ComfStand == 1) || (ComfStand == 10), !- Program Line 1 + if (RMOT >= AHSTall) && (RMOT <= ACSTaul), !- Program Line 2 + if (BLOCK1_ZONE1_OpT <= ACSTnoTol), !- Program Line 3 + if (BLOCK1_ZONE1_OpT >= AHSTnoTol), !- Program Line 4 + set ComfHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 5 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 6 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 7 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 8 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 9 + endif, !- Program Line 10 + elseif (BLOCK1_ZONE1_OpT > ACSTnoTol), !- Program Line 11 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 12 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 13 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 14 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 15 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 16 + elseif (BLOCK1_ZONE1_OpT < AHSTnoTol), !- Program Line 17 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 18 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 19 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 20 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 21 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 22 + endif, !- Program Line 23 + elseif (RMOT > ACSTaul), !- Program Line 24 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 25 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 26 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 27 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 28 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 29 + elseif (RMOT < AHSTall), !- Program Line 30 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 31 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 32 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 33 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 34 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + if (PMOT >= AHSTall) && (PMOT <= ACSTaul), !- Program Line 38 + if (BLOCK1_ZONE1_OpT <= ACSTnoTol), !- Program Line 39 + if (BLOCK1_ZONE1_OpT >= AHSTnoTol), !- Program Line 40 + set ComfHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 41 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 42 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 43 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 44 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 45 + endif, !- Program Line 46 + elseif (BLOCK1_ZONE1_OpT > ACSTnoTol), !- Program Line 47 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 48 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 49 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 50 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 51 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 52 + elseif (BLOCK1_ZONE1_OpT < AHSTnoTol), !- Program Line 53 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 54 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 55 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 56 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 57 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 58 + endif, !- Program Line 59 + elseif (PMOT > ACSTaul), !- Program Line 60 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 61 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 62 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 63 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 64 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 65 + elseif (PMOT < AHSTall), !- Program Line 66 + set ComfHours_BLOCK1_ZONE1 = 0, !- Program Line 67 + set DiscomfAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 68 + set DiscomfAppColdHours_BLOCK1_ZONE1 = 0, !- Program Line 69 + set DiscomfNonAppHotHours_BLOCK1_ZONE1 = 0, !- Program Line 70 + set DiscomfNonAppColdHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 71 + endif, !- Program Line 72 + endif; !- Program Line 73 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetAppLimits, !- Name + if ComfStand == 99, !- Program Line 1 + set ACSTaul = 33.5, !- Program Line 2 + set ACSTall = 10, !- Program Line 3 + set AHSTaul = 33.5, !- Program Line 4 + set AHSTall = 10, !- Program Line 5 + elseif ComfStand == 1, !- Program Line 6 + set ACSTaul = 30, !- Program Line 7 + set ACSTall = 10, !- Program Line 8 + set AHSTaul = 30, !- Program Line 9 + set AHSTall = 10, !- Program Line 10 + elseif ComfStand == 2 || ComfStand == 12, !- Program Line 11 + set ACSTaul = 33.5, !- Program Line 12 + set ACSTall = 10, !- Program Line 13 + set AHSTaul = 33.5, !- Program Line 14 + set AHSTall = 10, !- Program Line 15 + elseif ComfStand == 3, !- Program Line 16 + set ACSTaul = 30, !- Program Line 17 + set ACSTall = 5, !- Program Line 18 + set AHSTaul = 30, !- Program Line 19 + set AHSTall = 5, !- Program Line 20 + elseif (ComfStand == 4) || (ComfStand == 5), !- Program Line 21 + if CAT == 1, !- Program Line 22 + set ACSTaul = 28, !- Program Line 23 + set ACSTall = 18, !- Program Line 24 + set AHSTaul = 28, !- Program Line 25 + set AHSTall = 18, !- Program Line 26 + elseif CAT == 2, !- Program Line 27 + set ACSTaul = 30, !- Program Line 28 + set ACSTall = 18, !- Program Line 29 + set AHSTaul = 28, !- Program Line 30 + set AHSTall = 16, !- Program Line 31 + else, !- Program Line 32 + set ACSTaul = 50, !- Program Line 33 + set ACSTall = 50, !- Program Line 34 + set AHSTaul = 50, !- Program Line 35 + set AHSTall = 50, !- Program Line 36 + endif, !- Program Line 37 + elseif ComfStand == 6, !- Program Line 38 + set ACSTaul = 30, !- Program Line 39 + set ACSTall = -7, !- Program Line 40 + set AHSTaul = 30, !- Program Line 41 + set AHSTall = -7, !- Program Line 42 + elseif ComfStand == 7, !- Program Line 43 + set ACSTaul = 31, !- Program Line 44 + set ACSTall = 12.5, !- Program Line 45 + set AHSTaul = 31, !- Program Line 46 + set AHSTall = 12.5, !- Program Line 47 + elseif ComfStand == 8, !- Program Line 48 + set ACSTaul = 38.5, !- Program Line 49 + set ACSTall = 13, !- Program Line 50 + set AHSTaul = 38.5, !- Program Line 51 + set AHSTall = 13, !- Program Line 52 + elseif ComfStand == 9 || ComfStand == 10 || ComfStand == 11, !- Program Line 53 + set ACSTaul = 33, !- Program Line 54 + set ACSTall = 5.5, !- Program Line 55 + set AHSTaul = 33, !- Program Line 56 + set AHSTall = 5.5, !- Program Line 57 + elseif ComfStand == 13, !- Program Line 58 + set ACSTaul = 25, !- Program Line 59 + set ACSTall = 10, !- Program Line 60 + set AHSTaul = 25, !- Program Line 61 + set AHSTall = 10, !- Program Line 62 + elseif ComfStand == 14, !- Program Line 63 + set ACSTaul = 27, !- Program Line 64 + set ACSTall = 8, !- Program Line 65 + set AHSTaul = 27, !- Program Line 66 + set AHSTall = 8, !- Program Line 67 + elseif ComfStand == 15, !- Program Line 68 + set ACSTaul = 24.8, !- Program Line 69 + set ACSTall = 16.9, !- Program Line 70 + set AHSTaul = 24.8, !- Program Line 71 + set AHSTall = 16.9, !- Program Line 72 + elseif ComfStand == 16, !- Program Line 73 + set ACSTaul = 25.7, !- Program Line 74 + set ACSTall = 16.4, !- Program Line 75 + set AHSTaul = 25.7, !- Program Line 76 + set AHSTall = 16.4, !- Program Line 77 + elseif ComfStand == 17, !- Program Line 78 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 79 + set ACSTaul = 25.25, !- Program Line 80 + set ACSTall = 11.25, !- Program Line 81 + set AHSTaul = 25.25, !- Program Line 82 + set AHSTall = 11.25, !- Program Line 83 + else, !- Program Line 84 + set ACSTaul = 45, !- Program Line 85 + set ACSTall = 23, !- Program Line 86 + set AHSTaul = 45, !- Program Line 87 + set AHSTall = 23, !- Program Line 88 + endif, !- Program Line 89 + elseif ComfStand == 18, !- Program Line 90 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 91 + set ACSTaul = 27.5, !- Program Line 92 + set ACSTall = 15.5, !- Program Line 93 + set AHSTaul = 27.5, !- Program Line 94 + set AHSTall = 15.5, !- Program Line 95 + else, !- Program Line 96 + set ACSTaul = 34, !- Program Line 97 + set ACSTall = 23, !- Program Line 98 + set AHSTaul = 34, !- Program Line 99 + set AHSTall = 23, !- Program Line 100 + endif, !- Program Line 101 + elseif ComfStand == 19, !- Program Line 102 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 103 + set ACSTaul = 25.25, !- Program Line 104 + set ACSTall = 5, !- Program Line 105 + set AHSTaul = 25.25, !- Program Line 106 + set AHSTall = 5, !- Program Line 107 + else, !- Program Line 108 + set ACSTaul = 25.25, !- Program Line 109 + set ACSTall = 11.75, !- Program Line 110 + set AHSTaul = 25.25, !- Program Line 111 + set AHSTall = 11.75, !- Program Line 112 + endif, !- Program Line 113 + elseif ComfStand == 20, !- Program Line 114 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 115 + set ACSTaul = 29.75, !- Program Line 116 + set ACSTall = 13, !- Program Line 117 + set AHSTaul = 29.75, !- Program Line 118 + set AHSTall = 13, !- Program Line 119 + else, !- Program Line 120 + set ACSTaul = 45, !- Program Line 121 + set ACSTall = 23, !- Program Line 122 + set AHSTaul = 45, !- Program Line 123 + set AHSTall = 23, !- Program Line 124 + endif, !- Program Line 125 + elseif ComfStand == 21, !- Program Line 126 + set ACSTaul = 20, !- Program Line 127 + set ACSTall = 6.5, !- Program Line 128 + set AHSTaul = 20, !- Program Line 129 + set AHSTall = 6.5, !- Program Line 130 + else, !- Program Line 131 + set ACSTaul = 50, !- Program Line 132 + set ACSTall = 50, !- Program Line 133 + set AHSTaul = 50, !- Program Line 134 + set AHSTall = 50, !- Program Line 135 + endif; !- Program Line 136 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + ApplyCAT, !- Name + set CATcoolOffset = 0, !- Program Line 1 + set CATheatOffset = 0, !- Program Line 2 + if ComfStand == 99, !- Program Line 3 + set ACSToffset = 3.5+CATcoolOffset, !- Program Line 4 + set AHSToffset = -3.5+CATheatOffset, !- Program Line 5 + elseif (ComfStand == 1 ), !- Program Line 6 + if (CAT == 1), !- Program Line 7 + set ACSToffset = 2+CATcoolOffset, !- Program Line 8 + set AHSToffset = -3+CATheatOffset, !- Program Line 9 + elseif (CAT == 2), !- Program Line 10 + set ACSToffset = 3+CATcoolOffset, !- Program Line 11 + set AHSToffset = -4+CATheatOffset, !- Program Line 12 + elseif (CAT == 3), !- Program Line 13 + set ACSToffset = 4+CATcoolOffset, !- Program Line 14 + set AHSToffset = -5+CATheatOffset, !- Program Line 15 + endif, !- Program Line 16 + elseif ComfStand == 2 || ComfStand == 3 || ComfStand == 11, !- Program Line 17 + if (CAT == 90), !- Program Line 18 + set ACSToffset = 2.5+CATcoolOffset, !- Program Line 19 + set AHSToffset = -2.5+CATheatOffset, !- Program Line 20 + elseif (CAT == 80), !- Program Line 21 + set ACSToffset = 3.5+CATcoolOffset, !- Program Line 22 + set AHSToffset = -3.5+CATheatOffset, !- Program Line 23 + endif, !- Program Line 24 + elseif (ComfStand == 4 ) || (ComfStand == 5) || (ComfStand == 6), !- Program Line 25 + set ACSToffset = 0+CATcoolOffset, !- Program Line 26 + set AHSToffset = 0+CATheatOffset, !- Program Line 27 + elseif (ComfStand == 7), !- Program Line 28 + if (CAT == 90), !- Program Line 29 + set ACSToffset = 2.4+CATcoolOffset, !- Program Line 30 + set AHSToffset = -2.4+CATheatOffset, !- Program Line 31 + elseif (CAT == 85), !- Program Line 32 + set ACSToffset = 3.3+CATcoolOffset, !- Program Line 33 + set AHSToffset = -3.3+CATheatOffset, !- Program Line 34 + elseif (CAT == 80), !- Program Line 35 + set ACSToffset = 4.1+CATcoolOffset, !- Program Line 36 + set AHSToffset = -4.1+CATheatOffset, !- Program Line 37 + endif, !- Program Line 38 + elseif (ComfStand == 8), !- Program Line 39 + if (CAT == 90), !- Program Line 40 + set ACSToffset = 3.5+CATcoolOffset, !- Program Line 41 + set AHSToffset = -3.5+CATheatOffset, !- Program Line 42 + elseif (CAT == 85), !- Program Line 43 + set ACSToffset = 4.8+CATcoolOffset, !- Program Line 44 + set AHSToffset = -4.8+CATheatOffset, !- Program Line 45 + elseif (CAT == 80), !- Program Line 46 + set ACSToffset = 5.9+CATcoolOffset, !- Program Line 47 + set AHSToffset = -5.9+CATheatOffset, !- Program Line 48 + endif, !- Program Line 49 + elseif ComfStand == 9 || ComfStand == 10, !- Program Line 50 + if (CAT == 90), !- Program Line 51 + set ACSToffset = 2.15+CATcoolOffset, !- Program Line 52 + set AHSToffset = -2.15+CATheatOffset, !- Program Line 53 + elseif (CAT == 80), !- Program Line 54 + set ACSToffset = 3.6+CATcoolOffset, !- Program Line 55 + set AHSToffset = -3.6+CATheatOffset, !- Program Line 56 + endif, !- Program Line 57 + elseif ComfStand == 12, !- Program Line 58 + if (CAT == 90), !- Program Line 59 + set ACSToffset = 1.7+CATcoolOffset, !- Program Line 60 + set AHSToffset = -1.7+CATheatOffset, !- Program Line 61 + elseif (CAT == 80), !- Program Line 62 + set ACSToffset = 2.89+CATcoolOffset, !- Program Line 63 + set AHSToffset = -2.89+CATheatOffset, !- Program Line 64 + endif, !- Program Line 65 + elseif ComfStand == 13, !- Program Line 66 + if (CAT == 90), !- Program Line 67 + set ACSToffset = 3.45+CATcoolOffset, !- Program Line 68 + set AHSToffset = -3.45+CATheatOffset, !- Program Line 69 + elseif (CAT == 80), !- Program Line 70 + set ACSToffset = 4.55+CATcoolOffset, !- Program Line 71 + set AHSToffset = -4.55+CATheatOffset, !- Program Line 72 + endif, !- Program Line 73 + elseif ComfStand == 14, !- Program Line 74 + if (CAT == 90), !- Program Line 75 + set ACSToffset = 3.5+CATcoolOffset, !- Program Line 76 + set AHSToffset = -3.5+CATheatOffset, !- Program Line 77 + elseif (CAT == 80), !- Program Line 78 + set ACSToffset = 4.5+CATcoolOffset, !- Program Line 79 + set AHSToffset = -4.5+CATheatOffset, !- Program Line 80 + endif, !- Program Line 81 + elseif ComfStand == 15, !- Program Line 82 + if (CAT == 90), !- Program Line 83 + set ACSToffset = 2.8+CATcoolOffset, !- Program Line 84 + set AHSToffset = -2.8+CATheatOffset, !- Program Line 85 + elseif (CAT == 80), !- Program Line 86 + set ACSToffset = 3.8+CATcoolOffset, !- Program Line 87 + set AHSToffset = -3.8+CATheatOffset, !- Program Line 88 + endif, !- Program Line 89 + elseif ComfStand == 16, !- Program Line 90 + if (CAT == 90), !- Program Line 91 + set ACSToffset = 1.1+CATcoolOffset, !- Program Line 92 + set AHSToffset = -1.1+CATheatOffset, !- Program Line 93 + elseif (CAT == 80), !- Program Line 94 + set ACSToffset = 2.1+CATcoolOffset, !- Program Line 95 + set AHSToffset = -2.1+CATheatOffset, !- Program Line 96 + endif, !- Program Line 97 + elseif (ComfStand == 17) || (ComfStand == 18), !- Program Line 98 + if CAT == 90, !- Program Line 99 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 100 + set ACSToffset = 2+CATcoolOffset, !- Program Line 101 + set AHSToffset = -2+CATheatOffset, !- Program Line 102 + else, !- Program Line 103 + set ACSToffset = 2+CATcoolOffset, !- Program Line 104 + set AHSToffset = -2+CATheatOffset, !- Program Line 105 + endif, !- Program Line 106 + elseif CAT == 80, !- Program Line 107 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 108 + set ACSToffset = 3+CATcoolOffset, !- Program Line 109 + set AHSToffset = -3+CATheatOffset, !- Program Line 110 + else, !- Program Line 111 + set ACSToffset = 3+CATcoolOffset, !- Program Line 112 + set AHSToffset = -3+CATheatOffset, !- Program Line 113 + endif, !- Program Line 114 + endif, !- Program Line 115 + elseif ComfStand == 19, !- Program Line 116 + if CAT == 90, !- Program Line 117 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 118 + set ACSToffset = 2+CATcoolOffset, !- Program Line 119 + set AHSToffset = -2+CATheatOffset, !- Program Line 120 + else, !- Program Line 121 + set ACSToffset = 1+CATcoolOffset, !- Program Line 122 + set AHSToffset = -1+CATheatOffset, !- Program Line 123 + endif, !- Program Line 124 + elseif CAT == 80, !- Program Line 125 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 126 + set ACSToffset = 3+CATcoolOffset, !- Program Line 127 + set AHSToffset = -3+CATheatOffset, !- Program Line 128 + else, !- Program Line 129 + set ACSToffset = 2+CATcoolOffset, !- Program Line 130 + set AHSToffset = -2+CATheatOffset, !- Program Line 131 + endif, !- Program Line 132 + endif, !- Program Line 133 + elseif ComfStand == 20, !- Program Line 134 + if CAT == 90, !- Program Line 135 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 136 + set ACSToffset = 2+CATcoolOffset, !- Program Line 137 + set AHSToffset = -2+CATheatOffset, !- Program Line 138 + else, !- Program Line 139 + set ACSToffset = 5+CATcoolOffset, !- Program Line 140 + set AHSToffset = -5+CATheatOffset, !- Program Line 141 + endif, !- Program Line 142 + elseif CAT == 80, !- Program Line 143 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 144 + set ACSToffset = 3+CATcoolOffset, !- Program Line 145 + set AHSToffset = -3+CATheatOffset, !- Program Line 146 + else, !- Program Line 147 + set ACSToffset = 6+CATcoolOffset, !- Program Line 148 + set AHSToffset = -6+CATheatOffset, !- Program Line 149 + endif, !- Program Line 150 + endif, !- Program Line 151 + elseif ComfStand == 21, !- Program Line 152 + if (CAT == 90), !- Program Line 153 + set ACSToffset = 2.5+CATcoolOffset, !- Program Line 154 + set AHSToffset = -2.5+CATheatOffset, !- Program Line 155 + elseif (CAT == 80), !- Program Line 156 + set ACSToffset = 4+CATcoolOffset, !- Program Line 157 + set AHSToffset = -4+CATheatOffset, !- Program Line 158 + endif, !- Program Line 159 + endif; !- Program Line 160 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetAST, !- Name + set SetpointAcc = 10000, !- Program Line 1 + set m = 0.31, !- Program Line 2 + set n = 17.8, !- Program Line 3 + if CoolSeasonEnd > CoolSeasonStart, !- Program Line 4 + if (DayOfYear >= CoolSeasonStart) && (DayOfYear < CoolSeasonEnd), !- Program Line 5 + set CoolingSeason = 1, !- Program Line 6 + else, !- Program Line 7 + set CoolingSeason = 0, !- Program Line 8 + endif, !- Program Line 9 + elseif CoolSeasonStart > CoolSeasonEnd, !- Program Line 10 + if (DayOfYear >= CoolSeasonStart) || (DayOfYear < CoolSeasonEnd), !- Program Line 11 + set CoolingSeason = 1, !- Program Line 12 + else, !- Program Line 13 + set CoolingSeason = 0, !- Program Line 14 + endif, !- Program Line 15 + endif, !- Program Line 16 + if (ComfStand == 99) && (ComfMod == 3), !- Program Line 17 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 18 + set ACST = PMOT*m+n+ACSToffset+ACSTtol, !- Program Line 19 + elseif PMOT < ACSTall, !- Program Line 20 + set ACST = ACSTall*m+n+ACSToffset+ACSTtol, !- Program Line 21 + elseif PMOT > ACSTaul, !- Program Line 22 + set ACST = ACSTaul*m+n+ACSToffset+ACSTtol, !- Program Line 23 + endif, !- Program Line 24 + endif, !- Program Line 25 + if (ComfStand == 99) && (ComfMod == 3), !- Program Line 26 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 27 + set AHST = PMOT*m+n+AHSToffset+AHSTtol, !- Program Line 28 + elseif PMOT < AHSTall, !- Program Line 29 + set AHST = AHSTall*m+n+AHSToffset+AHSTtol, !- Program Line 30 + elseif PMOT > AHSTaul, !- Program Line 31 + set AHST = AHSTaul*m+n+AHSToffset+AHSTtol, !- Program Line 32 + endif, !- Program Line 33 + endif, !- Program Line 34 + if (ComfStand == 0) && (CurrentTime < 8), !- Program Line 35 + set ACST = 27+ACSTtol, !- Program Line 36 + set AHST = 17+AHSTtol, !- Program Line 37 + elseif (ComfStand == 0) && (CurrentTime < 16), !- Program Line 38 + set ACST = 25+ACSTtol, !- Program Line 39 + set AHST = 20+AHSTtol, !- Program Line 40 + elseif (ComfStand == 0) && (CurrentTime < 23), !- Program Line 41 + set ACST = 25+ACSTtol, !- Program Line 42 + set AHST = 20+AHSTtol, !- Program Line 43 + elseif (ComfStand == 0) && (CurrentTime < 24), !- Program Line 44 + set ACST = 27+ACSTtol, !- Program Line 45 + set AHST = 17+AHSTtol, !- Program Line 46 + endif, !- Program Line 47 + if (ComfStand == 1) && (ComfMod == 0), !- Program Line 48 + if CoolingSeason == 1, !- Program Line 49 + if (CAT==1), !- Program Line 50 + set ACST = 25.5+ACSTtol, !- Program Line 51 + elseif (CAT==2), !- Program Line 52 + set ACST = 26+ACSTtol, !- Program Line 53 + elseif (CAT==3), !- Program Line 54 + set ACST = 27+ACSTtol, !- Program Line 55 + endif, !- Program Line 56 + else, !- Program Line 57 + if (CAT==1), !- Program Line 58 + set ACST = 25+ACSTtol, !- Program Line 59 + elseif (CAT==2), !- Program Line 60 + set ACST = 25+ACSTtol, !- Program Line 61 + elseif (CAT==3), !- Program Line 62 + set ACST = 25+ACSTtol, !- Program Line 63 + endif, !- Program Line 64 + endif, !- Program Line 65 + endif, !- Program Line 66 + if (ComfStand == 1) && (ComfMod == 0), !- Program Line 67 + if CoolingSeason == 1, !- Program Line 68 + if (CAT==1), !- Program Line 69 + set AHST = 23.5+AHSTtol, !- Program Line 70 + elseif (CAT==2), !- Program Line 71 + set AHST = 23+AHSTtol, !- Program Line 72 + elseif (CAT==3), !- Program Line 73 + set AHST = 22+AHSTtol, !- Program Line 74 + endif, !- Program Line 75 + else, !- Program Line 76 + if (CAT==1), !- Program Line 77 + set AHST = 21+AHSTtol, !- Program Line 78 + elseif (CAT==2), !- Program Line 79 + set AHST = 20+AHSTtol, !- Program Line 80 + elseif (CAT==3), !- Program Line 81 + set AHST = 18+AHSTtol, !- Program Line 82 + endif, !- Program Line 83 + endif, !- Program Line 84 + endif, !- Program Line 85 + if (ComfStand == 1) && (ComfMod == 1), !- Program Line 86 + if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 87 + set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 88 + elseif CurrentTime < 7, !- Program Line 89 + set ACST = 27+ACSTtol, !- Program Line 90 + elseif CurrentTime < 15, !- Program Line 91 + set ACST = 50, !- Program Line 92 + elseif CurrentTime < 23, !- Program Line 93 + set ACST = 25+ACSTtol, !- Program Line 94 + elseif CurrentTime < 24, !- Program Line 95 + set ACST = 27+ACSTtol, !- Program Line 96 + endif, !- Program Line 97 + endif, !- Program Line 98 + if (ComfStand == 1) && (ComfMod == 1), !- Program Line 99 + if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 100 + set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 101 + elseif CurrentTime < 7, !- Program Line 102 + set AHST = 17+AHSTtol, !- Program Line 103 + elseif CurrentTime < 23, !- Program Line 104 + set AHST = 20+AHSTtol, !- Program Line 105 + elseif CurrentTime < 24, !- Program Line 106 + set AHST = 17+AHSTtol, !- Program Line 107 + endif, !- Program Line 108 + endif, !- Program Line 109 + if (ComfStand == 1) && (ComfMod == 2), !- Program Line 110 + if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 111 + set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 112 + elseif (RMOT < ACSTall) && (CAT==1), !- Program Line 113 + set ACST = 25+ACSTtol, !- Program Line 114 + elseif (RMOT > ACSTaul) && (CAT==1), !- Program Line 115 + set ACST = 25.5+ACSTtol, !- Program Line 116 + elseif (RMOT < ACSTall) && (CAT==2), !- Program Line 117 + set ACST = 25+ACSTtol, !- Program Line 118 + elseif (RMOT > ACSTaul) && (CAT==2), !- Program Line 119 + set ACST = 26+ACSTtol, !- Program Line 120 + elseif (RMOT < ACSTall) && (CAT==3), !- Program Line 121 + set ACST = 25+ACSTtol, !- Program Line 122 + elseif (RMOT > ACSTaul) && (CAT==3), !- Program Line 123 + set ACST = 27+ACSTtol, !- Program Line 124 + endif, !- Program Line 125 + endif, !- Program Line 126 + if (ComfStand == 1) && (ComfMod == 2), !- Program Line 127 + if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 128 + set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 129 + elseif (RMOT < AHSTall) && (CAT==1), !- Program Line 130 + set AHST = 21+AHSTtol, !- Program Line 131 + elseif (RMOT > AHSTaul) && (CAT==1), !- Program Line 132 + set AHST = 23.5+AHSTtol, !- Program Line 133 + elseif (RMOT < AHSTall) && (CAT==2), !- Program Line 134 + set AHST = 20+AHSTtol, !- Program Line 135 + elseif (RMOT > AHSTaul) && (CAT==2), !- Program Line 136 + set AHST = 23+AHSTtol, !- Program Line 137 + elseif (RMOT < AHSTall) && (CAT==3), !- Program Line 138 + set AHST = 18+AHSTtol, !- Program Line 139 + elseif (RMOT > AHSTaul) && (CAT==3), !- Program Line 140 + set AHST = 22+AHSTtol, !- Program Line 141 + endif, !- Program Line 142 + endif, !- Program Line 143 + if (ComfStand == 1) && (ComfMod == 3), !- Program Line 144 + if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 145 + set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 146 + elseif RMOT < ACSTall, !- Program Line 147 + set ACST = ACSTall*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 148 + elseif RMOT > ACSTaul, !- Program Line 149 + set ACST = ACSTaul*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 150 + endif, !- Program Line 151 + endif, !- Program Line 152 + if (ComfStand == 1) && (ComfMod == 3), !- Program Line 153 + if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 154 + set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 155 + elseif RMOT < AHSTall, !- Program Line 156 + set AHST = AHSTall*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 157 + elseif RMOT > AHSTaul, !- Program Line 158 + set AHST = AHSTaul*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 159 + endif, !- Program Line 160 + endif, !- Program Line 161 + if (ComfStand == 2) && (ComfMod == 0), !- Program Line 162 + if CoolingSeason == 1, !- Program Line 163 + if (CAT==80), !- Program Line 164 + set ACST = 27+ACSTtol, !- Program Line 165 + elseif (CAT==90), !- Program Line 166 + set ACST = 26+ACSTtol, !- Program Line 167 + endif, !- Program Line 168 + else, !- Program Line 169 + if (CAT==80), !- Program Line 170 + set ACST = 25+ACSTtol, !- Program Line 171 + elseif (CAT==90), !- Program Line 172 + set ACST = 24+ACSTtol, !- Program Line 173 + endif, !- Program Line 174 + endif, !- Program Line 175 + endif, !- Program Line 176 + if (ComfStand == 2) && (ComfMod == 0), !- Program Line 177 + if CoolingSeason == 1, !- Program Line 178 + if (CAT==80), !- Program Line 179 + set AHST = 22+AHSTtol, !- Program Line 180 + elseif (CAT==90), !- Program Line 181 + set AHST = 23+AHSTtol, !- Program Line 182 + endif, !- Program Line 183 + else, !- Program Line 184 + if (CAT==80), !- Program Line 185 + set AHST = 19+AHSTtol, !- Program Line 186 + elseif (CAT==90), !- Program Line 187 + set AHST = 20+AHSTtol, !- Program Line 188 + endif, !- Program Line 189 + endif, !- Program Line 190 + endif, !- Program Line 191 + if (ComfStand == 2) && (ComfMod == 1), !- Program Line 192 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 193 + set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 194 + elseif CurrentTime < 7, !- Program Line 195 + set ACST = 27+ACSTtol, !- Program Line 196 + elseif CurrentTime < 15, !- Program Line 197 + set ACST = 50, !- Program Line 198 + elseif CurrentTime < 23, !- Program Line 199 + set ACST = 25+ACSTtol, !- Program Line 200 + elseif CurrentTime < 24, !- Program Line 201 + set ACST = 27+ACSTtol, !- Program Line 202 + endif, !- Program Line 203 + endif, !- Program Line 204 + if (ComfStand == 2) && (ComfMod == 1), !- Program Line 205 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 206 + set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 207 + elseif CurrentTime < 7, !- Program Line 208 + set AHST = 17+AHSTtol, !- Program Line 209 + elseif CurrentTime < 23, !- Program Line 210 + set AHST = 20+AHSTtol, !- Program Line 211 + elseif CurrentTime < 24, !- Program Line 212 + set AHST = 17+AHSTtol, !- Program Line 213 + endif, !- Program Line 214 + endif, !- Program Line 215 + if (ComfStand == 2) && (ComfMod == 2), !- Program Line 216 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 217 + set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 218 + elseif CAT==80, !- Program Line 219 + if PMOT < ACSTall, !- Program Line 220 + set ACST = 25+ACSTtol, !- Program Line 221 + elseif PMOT > ACSTaul, !- Program Line 222 + set ACST = 27+ACSTtol, !- Program Line 223 + endif, !- Program Line 224 + elseif CAT==90, !- Program Line 225 + if PMOT < ACSTall, !- Program Line 226 + set ACST = 24+ACSTtol, !- Program Line 227 + elseif PMOT > ACSTaul, !- Program Line 228 + set ACST = 26+ACSTtol, !- Program Line 229 + endif, !- Program Line 230 + endif, !- Program Line 231 + endif, !- Program Line 232 + if (ComfStand == 2) && (ComfMod == 2), !- Program Line 233 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 234 + set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 235 + elseif CAT==80, !- Program Line 236 + if PMOT < AHSTall, !- Program Line 237 + set AHST = 19+AHSTtol, !- Program Line 238 + elseif PMOT > AHSTaul, !- Program Line 239 + set AHST = 22+AHSTtol, !- Program Line 240 + endif, !- Program Line 241 + elseif CAT==90, !- Program Line 242 + if PMOT < AHSTall, !- Program Line 243 + set AHST = 20+AHSTtol, !- Program Line 244 + elseif PMOT > AHSTaul, !- Program Line 245 + set AHST = 23+AHSTtol, !- Program Line 246 + endif, !- Program Line 247 + endif, !- Program Line 248 + endif, !- Program Line 249 + if (ComfStand == 2) && (ComfMod == 3), !- Program Line 250 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 251 + set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 252 + elseif PMOT < ACSTall, !- Program Line 253 + set ACST = ACSTall*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 254 + elseif PMOT > ACSTaul, !- Program Line 255 + set ACST = ACSTaul*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 256 + endif, !- Program Line 257 + endif, !- Program Line 258 + if (ComfStand == 2) && (ComfMod == 3), !- Program Line 259 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 260 + set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 261 + elseif PMOT < AHSTall, !- Program Line 262 + set AHST = AHSTall*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 263 + elseif PMOT > AHSTaul, !- Program Line 264 + set AHST = AHSTaul*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 265 + endif, !- Program Line 266 + endif, !- Program Line 267 + if (ComfStand == 3) && (ComfMod == 0), !- Program Line 268 + if (CAT==80), !- Program Line 269 + set ACST = 28+ACSTtol, !- Program Line 270 + elseif (CAT==90), !- Program Line 271 + set ACST = 27+ACSTtol, !- Program Line 272 + endif, !- Program Line 273 + endif, !- Program Line 274 + if (ComfStand == 3) && (ComfMod == 0), !- Program Line 275 + if (CAT==80), !- Program Line 276 + set AHST = 18+AHSTtol, !- Program Line 277 + elseif (CAT==90), !- Program Line 278 + set AHST = 19+AHSTtol, !- Program Line 279 + endif, !- Program Line 280 + endif, !- Program Line 281 + if (ComfStand == 3) && (ComfMod == 1), !- Program Line 282 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 283 + set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 284 + elseif CAT==80, !- Program Line 285 + if PMOT < ACSTall, !- Program Line 286 + set ACST = 28+ACSTtol, !- Program Line 287 + elseif PMOT > ACSTaul, !- Program Line 288 + set ACST = 28+ACSTtol, !- Program Line 289 + endif, !- Program Line 290 + elseif CAT==90, !- Program Line 291 + if PMOT < ACSTall, !- Program Line 292 + set ACST = 27+ACSTtol, !- Program Line 293 + elseif PMOT > ACSTaul, !- Program Line 294 + set ACST = 27+ACSTtol, !- Program Line 295 + endif, !- Program Line 296 + endif, !- Program Line 297 + endif, !- Program Line 298 + if (ComfStand == 3) && (ComfMod == 1), !- Program Line 299 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 300 + set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 301 + elseif CAT==80, !- Program Line 302 + if PMOT < AHSTall, !- Program Line 303 + set AHST = 18+AHSTtol, !- Program Line 304 + elseif PMOT > AHSTaul, !- Program Line 305 + set AHST = 18+AHSTtol, !- Program Line 306 + endif, !- Program Line 307 + elseif CAT==90, !- Program Line 308 + if PMOT < AHSTall, !- Program Line 309 + set AHST = 19+AHSTtol, !- Program Line 310 + elseif PMOT > AHSTaul, !- Program Line 311 + set AHST = 19+AHSTtol, !- Program Line 312 + endif, !- Program Line 313 + endif, !- Program Line 314 + endif, !- Program Line 315 + if (ComfStand == 3) && (ComfMod == 2), !- Program Line 316 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 317 + set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 318 + elseif CAT==80, !- Program Line 319 + if PMOT < ACSTall, !- Program Line 320 + set ACST = 25+ACSTtol, !- Program Line 321 + elseif PMOT > ACSTaul, !- Program Line 322 + set ACST = 27+ACSTtol, !- Program Line 323 + endif, !- Program Line 324 + elseif CAT==90, !- Program Line 325 + if PMOT < ACSTall, !- Program Line 326 + set ACST = 24+ACSTtol, !- Program Line 327 + elseif PMOT > ACSTaul, !- Program Line 328 + set ACST = 26+ACSTtol, !- Program Line 329 + endif, !- Program Line 330 + endif, !- Program Line 331 + endif, !- Program Line 332 + if (ComfStand == 3) && (ComfMod == 2), !- Program Line 333 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 334 + set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 335 + elseif CAT==80, !- Program Line 336 + if PMOT < AHSTall, !- Program Line 337 + set AHST = 19+AHSTtol, !- Program Line 338 + elseif PMOT > AHSTaul, !- Program Line 339 + set AHST = 22+AHSTtol, !- Program Line 340 + endif, !- Program Line 341 + elseif CAT==90, !- Program Line 342 + if PMOT < AHSTall, !- Program Line 343 + set AHST = 20+AHSTtol, !- Program Line 344 + elseif PMOT > AHSTaul, !- Program Line 345 + set AHST = 23+AHSTtol, !- Program Line 346 + endif, !- Program Line 347 + endif, !- Program Line 348 + endif, !- Program Line 349 + if (ComfStand == 3) && (ComfMod == 3), !- Program Line 350 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 351 + set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 352 + elseif PMOT < ACSTall, !- Program Line 353 + set ACST = ACSTall*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 354 + elseif PMOT > ACSTaul, !- Program Line 355 + set ACST = ACSTaul*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 356 + endif, !- Program Line 357 + endif, !- Program Line 358 + if (ComfStand == 3) && (ComfMod == 3), !- Program Line 359 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 360 + set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 361 + elseif PMOT < AHSTall, !- Program Line 362 + set AHST = AHSTall*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 363 + elseif PMOT > AHSTaul, !- Program Line 364 + set AHST = AHSTaul*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 365 + endif, !- Program Line 366 + endif, !- Program Line 367 + if (ComfStand == 4) && (ComfMod == 3), !- Program Line 368 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 369 + if CAT == 1, !- Program Line 370 + set ACST = PMOT*0.77+12.04+ACSTtol, !- Program Line 371 + elseif CAT == 2, !- Program Line 372 + set ACST = PMOT*0.73+15.28+ACSTtol, !- Program Line 373 + endif, !- Program Line 374 + elseif PMOT < ACSTall, !- Program Line 375 + if CAT == 1, !- Program Line 376 + set ACST = ACSTall*0.77+12.04+ACSTtol, !- Program Line 377 + elseif CAT == 2, !- Program Line 378 + set ACST = ACSTall*0.73+15.28+ACSTtol, !- Program Line 379 + endif, !- Program Line 380 + elseif PMOT > ACSTaul, !- Program Line 381 + if CAT == 1, !- Program Line 382 + set ACST = ACSTaul*0.77+12.04+ACSTtol, !- Program Line 383 + elseif CAT == 2, !- Program Line 384 + set ACST = ACSTaul*0.73+15.28+ACSTtol, !- Program Line 385 + endif, !- Program Line 386 + endif, !- Program Line 387 + endif, !- Program Line 388 + if (ComfStand == 4) && (ComfMod == 3), !- Program Line 389 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 390 + if CAT == 1, !- Program Line 391 + set AHST = PMOT*0.87+2.76+AHSTtol, !- Program Line 392 + elseif CAT == 2, !- Program Line 393 + set AHST = PMOT*0.91-0.48+AHSTtol, !- Program Line 394 + endif, !- Program Line 395 + elseif PMOT < AHSTall, !- Program Line 396 + if CAT == 1, !- Program Line 397 + set AHST = AHSTall*0.87+2.76+AHSTtol, !- Program Line 398 + elseif CAT == 2, !- Program Line 399 + set AHST = AHSTall*0.91-0.48+AHSTtol, !- Program Line 400 + endif, !- Program Line 401 + elseif PMOT > AHSTaul, !- Program Line 402 + if CAT == 1, !- Program Line 403 + set AHST = AHSTaul*0.87+2.76+AHSTtol, !- Program Line 404 + elseif CAT == 2, !- Program Line 405 + set AHST = AHSTaul*0.91-0.48+AHSTtol, !- Program Line 406 + endif, !- Program Line 407 + endif, !- Program Line 408 + endif, !- Program Line 409 + if (ComfStand == 5) && (ComfMod == 3), !- Program Line 410 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 411 + if CAT == 1, !- Program Line 412 + set ACST = PMOT*0.77+9.34+ACSTtol, !- Program Line 413 + elseif CAT == 2, !- Program Line 414 + set ACST = PMOT*0.73+12.72+ACSTtol, !- Program Line 415 + endif, !- Program Line 416 + elseif PMOT < ACSTall, !- Program Line 417 + if CAT == 1, !- Program Line 418 + set ACST = ACSTall*0.77+9.34+ACSTtol, !- Program Line 419 + elseif CAT == 2, !- Program Line 420 + set ACST = ACSTall*0.73+12.72+ACSTtol, !- Program Line 421 + endif, !- Program Line 422 + elseif PMOT > ACSTaul, !- Program Line 423 + if CAT == 1, !- Program Line 424 + set ACST = ACSTaul*0.77+9.34+ACSTtol, !- Program Line 425 + elseif CAT == 2, !- Program Line 426 + set ACST = ACSTaul*0.73+12.72+ACSTtol, !- Program Line 427 + endif, !- Program Line 428 + endif, !- Program Line 429 + endif, !- Program Line 430 + if (ComfStand == 5) && (ComfMod == 3), !- Program Line 431 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 432 + if CAT == 1, !- Program Line 433 + set AHST = PMOT*0.87-0.31+AHSTtol, !- Program Line 434 + elseif CAT == 2, !- Program Line 435 + set AHST = PMOT*0.91-3.69+AHSTtol, !- Program Line 436 + endif, !- Program Line 437 + elseif PMOT < AHSTall, !- Program Line 438 + if CAT == 1, !- Program Line 439 + set AHST = AHSTall*0.87-0.31+AHSTtol, !- Program Line 440 + elseif CAT == 2, !- Program Line 441 + set AHST = AHSTall*0.91-3.69+AHSTtol, !- Program Line 442 + endif, !- Program Line 443 + elseif PMOT > AHSTaul, !- Program Line 444 + if CAT == 1, !- Program Line 445 + set AHST = AHSTaul*0.87-0.31+AHSTtol, !- Program Line 446 + elseif CAT == 2, !- Program Line 447 + set AHST = AHSTaul*0.91-3.69+AHSTtol, !- Program Line 448 + endif, !- Program Line 449 + endif, !- Program Line 450 + endif, !- Program Line 451 + if (ComfStand == 6) && (ComfMod == 2), !- Program Line 452 + if CAT==80, !- Program Line 453 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 454 + set ACST = PMOT*0.30+25.9+ACSTtol, !- Program Line 455 + elseif PMOT < ACSTall, !- Program Line 456 + set ACST = 25+ACSTtol, !- Program Line 457 + elseif PMOT > ACSTaul, !- Program Line 458 + set ACST = 27+ACSTtol, !- Program Line 459 + endif, !- Program Line 460 + elseif CAT==90, !- Program Line 461 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 462 + set ACST = PMOT*0.30+23.6+ACSTtol, !- Program Line 463 + elseif PMOT < ACSTall, !- Program Line 464 + set ACST = 24+ACSTtol, !- Program Line 465 + elseif PMOT > ACSTaul, !- Program Line 466 + set ACST = 26+ACSTtol, !- Program Line 467 + endif, !- Program Line 468 + endif, !- Program Line 469 + endif, !- Program Line 470 + if (ComfStand == 6) && (ComfMod == 2), !- Program Line 471 + if CAT==80, !- Program Line 472 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 473 + set AHST = PMOT*0.30+25.9+AHSTtol, !- Program Line 474 + elseif PMOT < AHSTall, !- Program Line 475 + set AHST = 19+AHSTtol, !- Program Line 476 + elseif PMOT > AHSTaul, !- Program Line 477 + set AHST = 22+AHSTtol, !- Program Line 478 + endif, !- Program Line 479 + elseif CAT==90, !- Program Line 480 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 481 + set AHST = PMOT*0.30+23.6+AHSTtol, !- Program Line 482 + elseif PMOT < AHSTall, !- Program Line 483 + set AHST = 20+AHSTtol, !- Program Line 484 + elseif PMOT > AHSTaul, !- Program Line 485 + set AHST = 23+AHSTtol, !- Program Line 486 + endif, !- Program Line 487 + endif, !- Program Line 488 + endif, !- Program Line 489 + if (ComfStand == 6) && (ComfMod == 3), !- Program Line 490 + if CAT == 80, !- Program Line 491 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 492 + set ACST = PMOT*0.30+25.9+ACSTtol, !- Program Line 493 + elseif (PMOT < ACSTall), !- Program Line 494 + set ACST = ACSTall*0.30+25.9+ACSTtol, !- Program Line 495 + elseif (PMOT > ACSTaul), !- Program Line 496 + set ACST = ACSTaul*0.30+25.9+ACSTtol, !- Program Line 497 + endif, !- Program Line 498 + elseif CAT == 90, !- Program Line 499 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 500 + set ACST = PMOT*0.30+23.6+ACSTtol, !- Program Line 501 + elseif (PMOT < ACSTall), !- Program Line 502 + set ACST = ACSTall*0.30+23.6+ACSTtol, !- Program Line 503 + elseif (PMOT > ACSTaul), !- Program Line 504 + set ACST = ACSTaul*0.30+23.6+ACSTtol, !- Program Line 505 + endif, !- Program Line 506 + endif, !- Program Line 507 + endif, !- Program Line 508 + if (ComfStand == 6) && (ComfMod == 3), !- Program Line 509 + if CAT == 80, !- Program Line 510 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 511 + set AHST = PMOT*0.32+14.88+AHSTtol, !- Program Line 512 + elseif (PMOT < AHSTall), !- Program Line 513 + set AHST = AHSTall*0.32+14.88+AHSTtol, !- Program Line 514 + elseif (PMOT > AHSTaul), !- Program Line 515 + set AHST = AHSTaul*0.32+14.88+AHSTtol, !- Program Line 516 + endif, !- Program Line 517 + elseif CAT == 90, !- Program Line 518 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 519 + set AHST = PMOT*0.31+17.14+AHSTtol, !- Program Line 520 + elseif (PMOT < AHSTall), !- Program Line 521 + set AHST = AHSTall*0.31+17.14+AHSTtol, !- Program Line 522 + elseif (PMOT > AHSTaul), !- Program Line 523 + set AHST = AHSTaul*0.31+17.14+AHSTtol, !- Program Line 524 + endif, !- Program Line 525 + endif, !- Program Line 526 + endif, !- Program Line 527 + if ComfStand == 7 || ComfStand == 8 || ComfStand == 9 || ComfStand == 10, !- Program Line 528 + if (ComfMod == 0), !- Program Line 529 + if CAT==80, !- Program Line 530 + set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 531 + elseif CAT==85, !- Program Line 532 + set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 533 + elseif CAT==90, !- Program Line 534 + set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 535 + endif, !- Program Line 536 + endif, !- Program Line 537 + endif, !- Program Line 538 + if ComfStand == 7 || ComfStand == 8 || ComfStand == 9 || ComfStand == 10, !- Program Line 539 + if (ComfMod == 0), !- Program Line 540 + if CAT==80, !- Program Line 541 + set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 542 + elseif CAT==85, !- Program Line 543 + set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 544 + elseif CAT==90, !- Program Line 545 + set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 546 + endif, !- Program Line 547 + endif, !- Program Line 548 + endif, !- Program Line 549 + if (ComfStand == 7) && (ComfMod == 1), !- Program Line 550 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 551 + set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 552 + else, !- Program Line 553 + if CAT==80, !- Program Line 554 + set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 555 + elseif CAT==85, !- Program Line 556 + set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 557 + elseif CAT==90, !- Program Line 558 + set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 559 + endif, !- Program Line 560 + endif, !- Program Line 561 + endif, !- Program Line 562 + if (ComfStand == 7) && (ComfMod == 1), !- Program Line 563 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 564 + set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 565 + else, !- Program Line 566 + if CAT==80, !- Program Line 567 + set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 568 + elseif CAT==85, !- Program Line 569 + set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 570 + elseif CAT==90, !- Program Line 571 + set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 572 + endif, !- Program Line 573 + endif, !- Program Line 574 + endif, !- Program Line 575 + if (ComfStand == 7) && (ComfMod == 2), !- Program Line 576 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 577 + set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 578 + elseif CAT==80, !- Program Line 579 + if PMOT < ACSTall, !- Program Line 580 + set ACST = 25+ACSTtol, !- Program Line 581 + elseif PMOT > ACSTaul, !- Program Line 582 + set ACST = 27+ACSTtol, !- Program Line 583 + endif, !- Program Line 584 + elseif CAT==85, !- Program Line 585 + if PMOT < ACSTall, !- Program Line 586 + set ACST = 25.72+ACSTtol, !- Program Line 587 + elseif PMOT > ACSTaul, !- Program Line 588 + set ACST = 27.89+ACSTtol, !- Program Line 589 + endif, !- Program Line 590 + elseif CAT==90, !- Program Line 591 + if PMOT < ACSTall, !- Program Line 592 + set ACST = 24+ACSTtol, !- Program Line 593 + elseif PMOT > ACSTaul, !- Program Line 594 + set ACST = 26+ACSTtol, !- Program Line 595 + endif, !- Program Line 596 + endif, !- Program Line 597 + endif, !- Program Line 598 + if (ComfStand == 7) && (ComfMod == 2), !- Program Line 599 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 600 + set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 601 + elseif CAT==80, !- Program Line 602 + if PMOT < AHSTall, !- Program Line 603 + set AHST = 19+AHSTtol, !- Program Line 604 + elseif PMOT > AHSTaul, !- Program Line 605 + set AHST = 22+AHSTtol, !- Program Line 606 + endif, !- Program Line 607 + elseif CAT==85, !- Program Line 608 + if PMOT < AHSTall, !- Program Line 609 + set AHST = 20.77+AHSTtol, !- Program Line 610 + elseif PMOT > AHSTaul, !- Program Line 611 + set AHST = 24.26+AHSTtol, !- Program Line 612 + endif, !- Program Line 613 + elseif CAT==90, !- Program Line 614 + if PMOT < AHSTall, !- Program Line 615 + set AHST = 20+AHSTtol, !- Program Line 616 + elseif PMOT > AHSTaul, !- Program Line 617 + set AHST = 23+AHSTtol, !- Program Line 618 + endif, !- Program Line 619 + endif, !- Program Line 620 + endif, !- Program Line 621 + if (ComfStand == 7) && (ComfMod == 3), !- Program Line 622 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 623 + set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 624 + elseif PMOT < ACSTall, !- Program Line 625 + set ACST = ACSTall*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 626 + elseif PMOT > ACSTaul, !- Program Line 627 + set ACST = ACSTaul*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 628 + endif, !- Program Line 629 + endif, !- Program Line 630 + if (ComfStand == 7) && (ComfMod == 3), !- Program Line 631 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 632 + set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 633 + elseif PMOT < AHSTall, !- Program Line 634 + set AHST = AHSTall*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 635 + elseif PMOT > AHSTaul, !- Program Line 636 + set AHST = AHSTaul*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 637 + endif, !- Program Line 638 + endif, !- Program Line 639 + if (ComfStand == 8) && (ComfMod == 1), !- Program Line 640 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 641 + set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 642 + else, !- Program Line 643 + if CAT==80, !- Program Line 644 + set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 645 + elseif CAT==85, !- Program Line 646 + set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 647 + elseif CAT==90, !- Program Line 648 + set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 649 + endif, !- Program Line 650 + endif, !- Program Line 651 + endif, !- Program Line 652 + if (ComfStand == 8) && (ComfMod == 1), !- Program Line 653 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 654 + set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 655 + else, !- Program Line 656 + if CAT==80, !- Program Line 657 + set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 658 + elseif CAT==85, !- Program Line 659 + set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 660 + elseif CAT==90, !- Program Line 661 + set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 662 + endif, !- Program Line 663 + endif, !- Program Line 664 + endif, !- Program Line 665 + if (ComfStand == 8) && (ComfMod == 2), !- Program Line 666 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 667 + set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 668 + elseif CAT==80, !- Program Line 669 + if PMOT < ACSTall, !- Program Line 670 + set ACST = 25+ACSTtol, !- Program Line 671 + elseif PMOT > ACSTaul, !- Program Line 672 + set ACST = 27+ACSTtol, !- Program Line 673 + endif, !- Program Line 674 + elseif CAT==85, !- Program Line 675 + if PMOT < ACSTall, !- Program Line 676 + set ACST = 25.72+ACSTtol, !- Program Line 677 + elseif PMOT > ACSTaul, !- Program Line 678 + set ACST = 27.89+ACSTtol, !- Program Line 679 + endif, !- Program Line 680 + elseif CAT==90, !- Program Line 681 + if PMOT < ACSTall, !- Program Line 682 + set ACST = 24+ACSTtol, !- Program Line 683 + elseif PMOT > ACSTaul, !- Program Line 684 + set ACST = 26+ACSTtol, !- Program Line 685 + endif, !- Program Line 686 + endif, !- Program Line 687 + endif, !- Program Line 688 + if (ComfStand == 8) && (ComfMod == 2), !- Program Line 689 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 690 + set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 691 + elseif CAT==80, !- Program Line 692 + if PMOT < AHSTall, !- Program Line 693 + set AHST = 19+AHSTtol, !- Program Line 694 + elseif PMOT > AHSTaul, !- Program Line 695 + set AHST = 22+AHSTtol, !- Program Line 696 + endif, !- Program Line 697 + elseif CAT==85, !- Program Line 698 + if PMOT < AHSTall, !- Program Line 699 + set AHST = 20.77+AHSTtol, !- Program Line 700 + elseif PMOT > AHSTaul, !- Program Line 701 + set AHST = 24.26+AHSTtol, !- Program Line 702 + endif, !- Program Line 703 + elseif CAT==90, !- Program Line 704 + if PMOT < AHSTall, !- Program Line 705 + set AHST = 20+AHSTtol, !- Program Line 706 + elseif PMOT > AHSTaul, !- Program Line 707 + set AHST = 23+AHSTtol, !- Program Line 708 + endif, !- Program Line 709 + endif, !- Program Line 710 + endif, !- Program Line 711 + if (ComfStand == 8) && (ComfMod == 3), !- Program Line 712 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 713 + set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 714 + elseif PMOT < ACSTall, !- Program Line 715 + set ACST = ACSTall*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 716 + elseif PMOT > ACSTaul, !- Program Line 717 + set ACST = ACSTaul*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 718 + endif, !- Program Line 719 + endif, !- Program Line 720 + if (ComfStand == 8) && (ComfMod == 3), !- Program Line 721 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 722 + set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 723 + elseif PMOT < AHSTall, !- Program Line 724 + set AHST = AHSTall*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 725 + elseif PMOT > AHSTaul, !- Program Line 726 + set AHST = AHSTaul*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 727 + endif, !- Program Line 728 + endif, !- Program Line 729 + if (ComfStand == 9) && (ComfMod == 1), !- Program Line 730 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 731 + set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 732 + else, !- Program Line 733 + if CAT==80, !- Program Line 734 + set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 735 + elseif CAT==90, !- Program Line 736 + set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 737 + endif, !- Program Line 738 + endif, !- Program Line 739 + endif, !- Program Line 740 + if (ComfStand == 9) && (ComfMod == 1), !- Program Line 741 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 742 + set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 743 + else, !- Program Line 744 + if CAT==80, !- Program Line 745 + set AHST = PMOT*0.078+23.25+2.72+AHSTtol, !- Program Line 746 + elseif CAT==90, !- Program Line 747 + set AHST = PMOT*0.078+23.25+1.5+AHSTtol, !- Program Line 748 + endif, !- Program Line 749 + endif, !- Program Line 750 + endif, !- Program Line 751 + if (ComfStand == 9) && (ComfMod == 2), !- Program Line 752 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 753 + set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 754 + elseif CAT==80, !- Program Line 755 + if PMOT < ACSTall, !- Program Line 756 + set ACST = 25+ACSTtol, !- Program Line 757 + elseif PMOT > ACSTaul, !- Program Line 758 + set ACST = 27+ACSTtol, !- Program Line 759 + endif, !- Program Line 760 + elseif CAT==90, !- Program Line 761 + if PMOT < ACSTall, !- Program Line 762 + set ACST = 24+ACSTtol, !- Program Line 763 + elseif PMOT > ACSTaul, !- Program Line 764 + set ACST = 26+ACSTtol, !- Program Line 765 + endif, !- Program Line 766 + endif, !- Program Line 767 + endif, !- Program Line 768 + if (ComfStand == 9) && (ComfMod == 2), !- Program Line 769 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 770 + set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 771 + elseif CAT==80, !- Program Line 772 + if PMOT < AHSTall, !- Program Line 773 + set AHST = 19+AHSTtol, !- Program Line 774 + elseif PMOT > AHSTaul, !- Program Line 775 + set AHST = 22+AHSTtol, !- Program Line 776 + endif, !- Program Line 777 + elseif CAT==90, !- Program Line 778 + if PMOT < AHSTall, !- Program Line 779 + set AHST = 20+AHSTtol, !- Program Line 780 + elseif PMOT > AHSTaul, !- Program Line 781 + set AHST = 23+AHSTtol, !- Program Line 782 + endif, !- Program Line 783 + endif, !- Program Line 784 + endif, !- Program Line 785 + if (ComfStand == 9) && (ComfMod == 3), !- Program Line 786 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 787 + set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 788 + elseif PMOT < ACSTall, !- Program Line 789 + set ACST = ACSTall*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 790 + elseif PMOT > ACSTaul, !- Program Line 791 + set ACST = ACSTaul*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 792 + endif, !- Program Line 793 + endif, !- Program Line 794 + if (ComfStand == 9) && (ComfMod == 3), !- Program Line 795 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 796 + set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 797 + elseif PMOT < AHSTall, !- Program Line 798 + set AHST = AHSTall*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 799 + elseif PMOT > AHSTaul, !- Program Line 800 + set AHST = AHSTaul*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 801 + endif, !- Program Line 802 + endif, !- Program Line 803 + if (ComfStand == 10) && (ComfMod == 1), !- Program Line 804 + if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 805 + set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 806 + else, !- Program Line 807 + if CAT==80, !- Program Line 808 + set ACST = RMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 809 + elseif CAT==90, !- Program Line 810 + set ACST = RMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 811 + endif, !- Program Line 812 + endif, !- Program Line 813 + endif, !- Program Line 814 + if (ComfStand == 10) && (ComfMod == 1), !- Program Line 815 + if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 816 + set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 817 + else, !- Program Line 818 + if CAT==80, !- Program Line 819 + set AHST = RMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 820 + elseif CAT==90, !- Program Line 821 + set AHST = RMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 822 + endif, !- Program Line 823 + endif, !- Program Line 824 + endif, !- Program Line 825 + if (ComfStand == 10) && (ComfMod == 2), !- Program Line 826 + if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 827 + set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 828 + elseif CAT==80, !- Program Line 829 + if RMOT < ACSTall, !- Program Line 830 + set ACST = 25+ACSTtol, !- Program Line 831 + elseif RMOT > ACSTaul, !- Program Line 832 + set ACST = 27+ACSTtol, !- Program Line 833 + endif, !- Program Line 834 + elseif CAT==90, !- Program Line 835 + if RMOT < ACSTall, !- Program Line 836 + set ACST = 24+ACSTtol, !- Program Line 837 + elseif RMOT > ACSTaul, !- Program Line 838 + set ACST = 26+ACSTtol, !- Program Line 839 + endif, !- Program Line 840 + endif, !- Program Line 841 + endif, !- Program Line 842 + if (ComfStand == 10) && (ComfMod == 2), !- Program Line 843 + if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 844 + set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 845 + elseif CAT==80, !- Program Line 846 + if RMOT < AHSTall, !- Program Line 847 + set AHST = 19+AHSTtol, !- Program Line 848 + elseif RMOT > AHSTaul, !- Program Line 849 + set AHST = 22+AHSTtol, !- Program Line 850 + endif, !- Program Line 851 + elseif CAT==90, !- Program Line 852 + if RMOT < AHSTall, !- Program Line 853 + set AHST = 20+AHSTtol, !- Program Line 854 + elseif RMOT > AHSTaul, !- Program Line 855 + set AHST = 23+AHSTtol, !- Program Line 856 + endif, !- Program Line 857 + endif, !- Program Line 858 + endif, !- Program Line 859 + if (ComfStand == 10) && (ComfMod == 3), !- Program Line 860 + if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 861 + set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 862 + elseif RMOT < ACSTall, !- Program Line 863 + set ACST = ACSTall*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 864 + elseif RMOT > ACSTaul, !- Program Line 865 + set ACST = ACSTaul*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 866 + endif, !- Program Line 867 + endif, !- Program Line 868 + if (ComfStand == 10) && (ComfMod == 3), !- Program Line 869 + if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 870 + set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 871 + elseif RMOT < AHSTall, !- Program Line 872 + set AHST = AHSTall*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 873 + elseif RMOT > AHSTaul, !- Program Line 874 + set AHST = AHSTaul*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 875 + endif, !- Program Line 876 + endif, !- Program Line 877 + if (ComfStand == 11) && (ComfMod == 1), !- Program Line 878 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 879 + set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 880 + else, !- Program Line 881 + if CAT==80, !- Program Line 882 + set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 883 + elseif CAT==90, !- Program Line 884 + set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 885 + endif, !- Program Line 886 + endif, !- Program Line 887 + endif, !- Program Line 888 + if (ComfStand == 11) && (ComfMod == 1), !- Program Line 889 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 890 + set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 891 + else, !- Program Line 892 + if CAT==80, !- Program Line 893 + set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 894 + elseif CAT==90, !- Program Line 895 + set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 896 + endif, !- Program Line 897 + endif, !- Program Line 898 + endif, !- Program Line 899 + if (ComfStand == 11) && (ComfMod == 2), !- Program Line 900 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 901 + set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 902 + elseif CAT==80, !- Program Line 903 + if PMOT < ACSTall, !- Program Line 904 + set ACST = 25+ACSTtol, !- Program Line 905 + elseif PMOT > ACSTaul, !- Program Line 906 + set ACST = 27+ACSTtol, !- Program Line 907 + endif, !- Program Line 908 + elseif CAT==90, !- Program Line 909 + if PMOT < ACSTall, !- Program Line 910 + set ACST = 24+ACSTtol, !- Program Line 911 + elseif PMOT > ACSTaul, !- Program Line 912 + set ACST = 26+ACSTtol, !- Program Line 913 + endif, !- Program Line 914 + endif, !- Program Line 915 + endif, !- Program Line 916 + if (ComfStand == 11) && (ComfMod == 2), !- Program Line 917 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 918 + set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 919 + elseif CAT==80, !- Program Line 920 + if PMOT < AHSTall, !- Program Line 921 + set AHST = 19+AHSTtol, !- Program Line 922 + elseif PMOT > AHSTaul, !- Program Line 923 + set AHST = 22+AHSTtol, !- Program Line 924 + endif, !- Program Line 925 + elseif CAT==90, !- Program Line 926 + if PMOT < AHSTall, !- Program Line 927 + set AHST = 20+AHSTtol, !- Program Line 928 + elseif PMOT > AHSTaul, !- Program Line 929 + set AHST = 23+AHSTtol, !- Program Line 930 + endif, !- Program Line 931 + endif, !- Program Line 932 + endif, !- Program Line 933 + if (ComfStand == 11) && (ComfMod == 3), !- Program Line 934 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 935 + set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 936 + elseif PMOT < ACSTall, !- Program Line 937 + set ACST = ACSTall*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 938 + elseif PMOT > ACSTaul, !- Program Line 939 + set ACST = ACSTaul*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 940 + endif, !- Program Line 941 + endif, !- Program Line 942 + if (ComfStand == 11) && (ComfMod == 3), !- Program Line 943 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 944 + set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 945 + elseif PMOT < AHSTall, !- Program Line 946 + set AHST = AHSTall*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 947 + elseif PMOT > AHSTaul, !- Program Line 948 + set AHST = AHSTaul*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 949 + endif, !- Program Line 950 + endif, !- Program Line 951 + if (ComfStand == 12) && (ComfMod == 0), !- Program Line 952 + if (CAT==80), !- Program Line 953 + set ACST = 27+ACSTtol, !- Program Line 954 + elseif (CAT==90), !- Program Line 955 + set ACST = 25.5+ACSTtol, !- Program Line 956 + endif, !- Program Line 957 + endif, !- Program Line 958 + if (ComfStand == 12) && (ComfMod == 0), !- Program Line 959 + if (CAT==80), !- Program Line 960 + set AHST = 20+AHSTtol, !- Program Line 961 + elseif (CAT==90), !- Program Line 962 + set AHST = 21.5+AHSTtol, !- Program Line 963 + endif, !- Program Line 964 + endif, !- Program Line 965 + if (ComfStand == 12) && (ComfMod == 1), !- Program Line 966 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 967 + set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 968 + elseif CAT==80, !- Program Line 969 + if PMOT < ACSTall, !- Program Line 970 + set ACST = 27+ACSTtol, !- Program Line 971 + elseif PMOT > ACSTaul, !- Program Line 972 + set ACST = 27+ACSTtol, !- Program Line 973 + endif, !- Program Line 974 + elseif CAT==90, !- Program Line 975 + if PMOT < ACSTall, !- Program Line 976 + set ACST = 25.5+ACSTtol, !- Program Line 977 + elseif PMOT > ACSTaul, !- Program Line 978 + set ACST = 25.5+ACSTtol, !- Program Line 979 + endif, !- Program Line 980 + endif, !- Program Line 981 + endif, !- Program Line 982 + if (ComfStand == 12) && (ComfMod == 1), !- Program Line 983 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 984 + set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 985 + elseif CAT==80, !- Program Line 986 + if PMOT < AHSTall, !- Program Line 987 + set AHST = 20+AHSTtol, !- Program Line 988 + elseif PMOT > AHSTaul, !- Program Line 989 + set AHST = 20+AHSTtol, !- Program Line 990 + endif, !- Program Line 991 + elseif CAT==90, !- Program Line 992 + if PMOT < AHSTall, !- Program Line 993 + set AHST = 21.5+AHSTtol, !- Program Line 994 + elseif PMOT > AHSTaul, !- Program Line 995 + set AHST = 21.5+AHSTtol, !- Program Line 996 + endif, !- Program Line 997 + endif, !- Program Line 998 + endif, !- Program Line 999 + if (ComfStand == 12) && (ComfMod == 2), !- Program Line 1000 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1001 + set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1002 + elseif CAT==80, !- Program Line 1003 + if PMOT < ACSTall, !- Program Line 1004 + set ACST = 25+ACSTtol, !- Program Line 1005 + elseif PMOT > ACSTaul, !- Program Line 1006 + set ACST = 27+ACSTtol, !- Program Line 1007 + endif, !- Program Line 1008 + elseif CAT==90, !- Program Line 1009 + if PMOT < ACSTall, !- Program Line 1010 + set ACST = 24+ACSTtol, !- Program Line 1011 + elseif PMOT > ACSTaul, !- Program Line 1012 + set ACST = 26+ACSTtol, !- Program Line 1013 + endif, !- Program Line 1014 + endif, !- Program Line 1015 + endif, !- Program Line 1016 + if (ComfStand == 12) && (ComfMod == 2), !- Program Line 1017 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1018 + set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1019 + elseif CAT==80, !- Program Line 1020 + if PMOT < AHSTall, !- Program Line 1021 + set AHST = 19+AHSTtol, !- Program Line 1022 + elseif PMOT > AHSTaul, !- Program Line 1023 + set AHST = 22+AHSTtol, !- Program Line 1024 + endif, !- Program Line 1025 + elseif CAT==90, !- Program Line 1026 + if PMOT < AHSTall, !- Program Line 1027 + set AHST = 20+AHSTtol, !- Program Line 1028 + elseif PMOT > AHSTaul, !- Program Line 1029 + set AHST = 23+AHSTtol, !- Program Line 1030 + endif, !- Program Line 1031 + endif, !- Program Line 1032 + endif, !- Program Line 1033 + if (ComfStand == 12) && (ComfMod == 3), !- Program Line 1034 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1035 + set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1036 + elseif PMOT < ACSTall, !- Program Line 1037 + set ACST = ACSTall*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1038 + elseif PMOT > ACSTaul, !- Program Line 1039 + set ACST = ACSTaul*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1040 + endif, !- Program Line 1041 + endif, !- Program Line 1042 + if (ComfStand == 12) && (ComfMod == 3), !- Program Line 1043 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1044 + set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1045 + elseif PMOT < AHSTall, !- Program Line 1046 + set AHST = AHSTall*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1047 + elseif PMOT > AHSTaul, !- Program Line 1048 + set AHST = AHSTaul*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1049 + endif, !- Program Line 1050 + endif, !- Program Line 1051 + if (ComfStand == 13) || (ComfStand == 14), !- Program Line 1052 + if (CAT==80), !- Program Line 1053 + if (CurrentTime > 6) && (CurrentTime < 23), !- Program Line 1054 + if (ComfMod == 0.1), !- Program Line 1055 + set ACST = 27+ACSTtol, !- Program Line 1056 + elseif (ComfMod == 0.2), !- Program Line 1057 + set ACST = 26+ACSTtol, !- Program Line 1058 + elseif (ComfMod == 0.3), !- Program Line 1059 + set ACST = 25+ACSTtol, !- Program Line 1060 + elseif (ComfMod == 0.4), !- Program Line 1061 + set ACST = 24+ACSTtol, !- Program Line 1062 + elseif (ComfMod == 0.5), !- Program Line 1063 + set ACST = 23+ACSTtol, !- Program Line 1064 + endif, !- Program Line 1065 + else, !- Program Line 1066 + set ACST = 24+ACSTtol, !- Program Line 1067 + endif, !- Program Line 1068 + elseif (CAT==90), !- Program Line 1069 + if (CurrentTime > 6) && (CurrentTime < 23), !- Program Line 1070 + if (ComfMod == 0.1), !- Program Line 1071 + set ACST = 26+ACSTtol, !- Program Line 1072 + elseif (ComfMod == 0.2), !- Program Line 1073 + set ACST = 25+ACSTtol, !- Program Line 1074 + elseif (ComfMod == 0.3), !- Program Line 1075 + set ACST = 24+ACSTtol, !- Program Line 1076 + elseif (ComfMod == 0.4), !- Program Line 1077 + set ACST = 23+ACSTtol, !- Program Line 1078 + elseif (ComfMod == 0.5), !- Program Line 1079 + set ACST = 22+ACSTtol, !- Program Line 1080 + endif, !- Program Line 1081 + else, !- Program Line 1082 + set ACST = 23+ACSTtol, !- Program Line 1083 + endif, !- Program Line 1084 + endif, !- Program Line 1085 + endif, !- Program Line 1086 + if (ComfStand == 13) || (ComfStand == 14), !- Program Line 1087 + if (ComfMod == 0.1) || (ComfMod == 0.2) || (ComfMod == 0.3) || (ComfMod == 0.4) || (ComfMod == 0.5), !- Program Line 1088 + if (CAT==80), !- Program Line 1089 + if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1090 + set AHST = 20+AHSTtol, !- Program Line 1091 + else, !- Program Line 1092 + set AHST = 18+AHSTtol, !- Program Line 1093 + endif, !- Program Line 1094 + elseif (CAT==90), !- Program Line 1095 + if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1096 + set AHST = 21+AHSTtol, !- Program Line 1097 + else, !- Program Line 1098 + set AHST = 19+AHSTtol, !- Program Line 1099 + endif, !- Program Line 1100 + endif, !- Program Line 1101 + endif, !- Program Line 1102 + endif, !- Program Line 1103 + if (ComfStand == 13), !- Program Line 1104 + if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1105 + if CAT == 80, !- Program Line 1106 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1107 + set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1108 + elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1109 + if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1110 + set ACST = 27+ACSTtol, !- Program Line 1111 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1112 + set ACST = 26+ACSTtol, !- Program Line 1113 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1114 + set ACST = 25+ACSTtol, !- Program Line 1115 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1116 + set ACST = 24+ACSTtol, !- Program Line 1117 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1118 + set ACST = 23+ACSTtol, !- Program Line 1119 + else, !- Program Line 1120 + set ACST = 24+ACSTtol, !- Program Line 1121 + endif, !- Program Line 1122 + endif, !- Program Line 1123 + elseif CAT==90, !- Program Line 1124 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1125 + set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1126 + elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1127 + if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1128 + set ACST = 26+ACSTtol, !- Program Line 1129 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1130 + set ACST = 25+ACSTtol, !- Program Line 1131 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1132 + set ACST = 24+ACSTtol, !- Program Line 1133 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1134 + set ACST = 23+ACSTtol, !- Program Line 1135 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1136 + set ACST = 22+ACSTtol, !- Program Line 1137 + else, !- Program Line 1138 + set ACST = 23+ACSTtol, !- Program Line 1139 + endif, !- Program Line 1140 + endif, !- Program Line 1141 + endif, !- Program Line 1142 + endif, !- Program Line 1143 + endif, !- Program Line 1144 + if (ComfStand == 13), !- Program Line 1145 + if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1146 + if CAT == 80, !- Program Line 1147 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1148 + set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1149 + elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1150 + if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1151 + set AHST = 20+AHSTtol, !- Program Line 1152 + else, !- Program Line 1153 + set AHST = 18+AHSTtol, !- Program Line 1154 + endif, !- Program Line 1155 + endif, !- Program Line 1156 + elseif CAT==90, !- Program Line 1157 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1158 + set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1159 + elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1160 + if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1161 + set AHST = 21+AHSTtol, !- Program Line 1162 + else, !- Program Line 1163 + set AHST = 19+AHSTtol, !- Program Line 1164 + endif, !- Program Line 1165 + endif, !- Program Line 1166 + endif, !- Program Line 1167 + endif, !- Program Line 1168 + endif, !- Program Line 1169 + if (ComfStand == 13) && (ComfMod == 2), !- Program Line 1170 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1171 + set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1172 + elseif CAT==80, !- Program Line 1173 + if PMOT < ACSTall, !- Program Line 1174 + set ACST = 25+ACSTtol, !- Program Line 1175 + elseif PMOT > ACSTaul, !- Program Line 1176 + set ACST = 27+ACSTtol, !- Program Line 1177 + endif, !- Program Line 1178 + elseif CAT==90, !- Program Line 1179 + if PMOT < ACSTall, !- Program Line 1180 + set ACST = 24+ACSTtol, !- Program Line 1181 + elseif PMOT > ACSTaul, !- Program Line 1182 + set ACST = 26+ACSTtol, !- Program Line 1183 + endif, !- Program Line 1184 + endif, !- Program Line 1185 + endif, !- Program Line 1186 + if (ComfStand == 13) && (ComfMod == 2), !- Program Line 1187 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1188 + set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1189 + elseif CAT==80, !- Program Line 1190 + if PMOT < AHSTall, !- Program Line 1191 + set AHST = 19+AHSTtol, !- Program Line 1192 + elseif PMOT > AHSTaul, !- Program Line 1193 + set AHST = 22+AHSTtol, !- Program Line 1194 + endif, !- Program Line 1195 + elseif CAT==90, !- Program Line 1196 + if PMOT < AHSTall, !- Program Line 1197 + set AHST = 20+AHSTtol, !- Program Line 1198 + elseif PMOT > AHSTaul, !- Program Line 1199 + set AHST = 23+AHSTtol, !- Program Line 1200 + endif, !- Program Line 1201 + endif, !- Program Line 1202 + endif, !- Program Line 1203 + if (ComfStand == 13) && (ComfMod == 3), !- Program Line 1204 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1205 + set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1206 + elseif PMOT < ACSTall, !- Program Line 1207 + set ACST = ACSTall*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1208 + elseif PMOT > ACSTaul, !- Program Line 1209 + set ACST = ACSTaul*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1210 + endif, !- Program Line 1211 + endif, !- Program Line 1212 + if (ComfStand == 13) && (ComfMod == 3), !- Program Line 1213 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1214 + set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1215 + elseif PMOT < AHSTall, !- Program Line 1216 + set AHST = AHSTall*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1217 + elseif PMOT > AHSTaul, !- Program Line 1218 + set AHST = AHSTaul*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1219 + endif, !- Program Line 1220 + endif, !- Program Line 1221 + if (ComfStand == 14), !- Program Line 1222 + if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1223 + if CAT == 80, !- Program Line 1224 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1225 + set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1226 + elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1227 + if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1228 + set ACST = 27+ACSTtol, !- Program Line 1229 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1230 + set ACST = 26+ACSTtol, !- Program Line 1231 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1232 + set ACST = 25+ACSTtol, !- Program Line 1233 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1234 + set ACST = 24+ACSTtol, !- Program Line 1235 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1236 + set ACST = 23+ACSTtol, !- Program Line 1237 + else, !- Program Line 1238 + set ACST = 24+ACSTtol, !- Program Line 1239 + endif, !- Program Line 1240 + endif, !- Program Line 1241 + elseif CAT==90, !- Program Line 1242 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1243 + set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1244 + elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1245 + if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1246 + set ACST = 26+ACSTtol, !- Program Line 1247 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1248 + set ACST = 25+ACSTtol, !- Program Line 1249 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1250 + set ACST = 24+ACSTtol, !- Program Line 1251 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1252 + set ACST = 23+ACSTtol, !- Program Line 1253 + elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1254 + set ACST = 22+ACSTtol, !- Program Line 1255 + else, !- Program Line 1256 + set ACST = 23+ACSTtol, !- Program Line 1257 + endif, !- Program Line 1258 + endif, !- Program Line 1259 + endif, !- Program Line 1260 + endif, !- Program Line 1261 + endif, !- Program Line 1262 + if (ComfStand == 14), !- Program Line 1263 + if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1264 + if CAT == 80, !- Program Line 1265 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1266 + set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1267 + elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1268 + if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1269 + set AHST = 20+AHSTtol, !- Program Line 1270 + else, !- Program Line 1271 + set AHST = 18+AHSTtol, !- Program Line 1272 + endif, !- Program Line 1273 + endif, !- Program Line 1274 + elseif CAT==90, !- Program Line 1275 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1276 + set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1277 + elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1278 + if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1279 + set AHST = 21+AHSTtol, !- Program Line 1280 + else, !- Program Line 1281 + set AHST = 19+AHSTtol, !- Program Line 1282 + endif, !- Program Line 1283 + endif, !- Program Line 1284 + endif, !- Program Line 1285 + endif, !- Program Line 1286 + endif, !- Program Line 1287 + if (ComfStand == 14) && (ComfMod == 2), !- Program Line 1288 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1289 + set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1290 + elseif CAT==80, !- Program Line 1291 + if PMOT < ACSTall, !- Program Line 1292 + set ACST = 25+ACSTtol, !- Program Line 1293 + elseif PMOT > ACSTaul, !- Program Line 1294 + set ACST = 27+ACSTtol, !- Program Line 1295 + endif, !- Program Line 1296 + elseif CAT==90, !- Program Line 1297 + if PMOT < ACSTall, !- Program Line 1298 + set ACST = 24+ACSTtol, !- Program Line 1299 + elseif PMOT > ACSTaul, !- Program Line 1300 + set ACST = 26+ACSTtol, !- Program Line 1301 + endif, !- Program Line 1302 + endif, !- Program Line 1303 + endif, !- Program Line 1304 + if (ComfStand == 14) && (ComfMod == 2), !- Program Line 1305 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1306 + set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1307 + elseif CAT==80, !- Program Line 1308 + if PMOT < AHSTall, !- Program Line 1309 + set AHST = 19+AHSTtol, !- Program Line 1310 + elseif PMOT > AHSTaul, !- Program Line 1311 + set AHST = 22+AHSTtol, !- Program Line 1312 + endif, !- Program Line 1313 + elseif CAT==90, !- Program Line 1314 + if PMOT < AHSTall, !- Program Line 1315 + set AHST = 20+AHSTtol, !- Program Line 1316 + elseif PMOT > AHSTaul, !- Program Line 1317 + set AHST = 23+AHSTtol, !- Program Line 1318 + endif, !- Program Line 1319 + endif, !- Program Line 1320 + endif, !- Program Line 1321 + if (ComfStand == 14) && (ComfMod == 3), !- Program Line 1322 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1323 + set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1324 + elseif PMOT < ACSTall, !- Program Line 1325 + set ACST = ACSTall*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1326 + elseif PMOT > ACSTaul, !- Program Line 1327 + set ACST = ACSTaul*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1328 + endif, !- Program Line 1329 + endif, !- Program Line 1330 + if (ComfStand == 14) && (ComfMod == 3), !- Program Line 1331 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1332 + set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1333 + elseif PMOT < AHSTall, !- Program Line 1334 + set AHST = AHSTall*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1335 + elseif PMOT > AHSTaul, !- Program Line 1336 + set AHST = AHSTaul*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1337 + endif, !- Program Line 1338 + endif, !- Program Line 1339 + if (ComfStand == 15) || (ComfStand == 16), !- Program Line 1340 + if (ComfMod == 0), !- Program Line 1341 + if (CAT==80), !- Program Line 1342 + if PMOT < 20, !- Program Line 1343 + set ACST = 23.5+ACSTtol, !- Program Line 1344 + set AHST = 21+ACSTtol, !- Program Line 1345 + else, !- Program Line 1346 + set ACST = 25.5+ACSTtol, !- Program Line 1347 + set AHST = 22.5+ACSTtol, !- Program Line 1348 + endif, !- Program Line 1349 + elseif (CAT==90), !- Program Line 1350 + if PMOT < 20, !- Program Line 1351 + set ACST = 23+ACSTtol, !- Program Line 1352 + set AHST = 21.5+ACSTtol, !- Program Line 1353 + else, !- Program Line 1354 + set ACST = 25+ACSTtol, !- Program Line 1355 + set AHST = 23+ACSTtol, !- Program Line 1356 + endif, !- Program Line 1357 + endif, !- Program Line 1358 + endif, !- Program Line 1359 + endif, !- Program Line 1360 + if (ComfStand == 15) && (ComfMod == 1), !- Program Line 1361 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1362 + set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1363 + elseif CAT==80, !- Program Line 1364 + if PMOT < ACSTall, !- Program Line 1365 + set ACST = 23.5+ACSTtol, !- Program Line 1366 + elseif PMOT > ACSTaul, !- Program Line 1367 + set ACST = 25.5+ACSTtol, !- Program Line 1368 + endif, !- Program Line 1369 + elseif CAT==90, !- Program Line 1370 + if PMOT < ACSTall, !- Program Line 1371 + set ACST = 23+ACSTtol, !- Program Line 1372 + elseif PMOT > ACSTaul, !- Program Line 1373 + set ACST = 25+ACSTtol, !- Program Line 1374 + endif, !- Program Line 1375 + endif, !- Program Line 1376 + endif, !- Program Line 1377 + if (ComfStand == 15) && (ComfMod == 1), !- Program Line 1378 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1379 + set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1380 + elseif CAT==80, !- Program Line 1381 + if PMOT < AHSTall, !- Program Line 1382 + set AHST = 21+AHSTtol, !- Program Line 1383 + elseif PMOT > AHSTaul, !- Program Line 1384 + set AHST = 22.5+AHSTtol, !- Program Line 1385 + endif, !- Program Line 1386 + elseif CAT==90, !- Program Line 1387 + if PMOT < AHSTall, !- Program Line 1388 + set AHST = 23+AHSTtol, !- Program Line 1389 + elseif PMOT > AHSTaul, !- Program Line 1390 + set AHST = 23+AHSTtol, !- Program Line 1391 + endif, !- Program Line 1392 + endif, !- Program Line 1393 + endif, !- Program Line 1394 + if (ComfStand == 15) && (ComfMod == 2), !- Program Line 1395 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1396 + set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1397 + elseif CAT==80, !- Program Line 1398 + if PMOT < ACSTall, !- Program Line 1399 + set ACST = 25+ACSTtol, !- Program Line 1400 + elseif PMOT > ACSTaul, !- Program Line 1401 + set ACST = 27+ACSTtol, !- Program Line 1402 + endif, !- Program Line 1403 + elseif CAT==90, !- Program Line 1404 + if PMOT < ACSTall, !- Program Line 1405 + set ACST = 24+ACSTtol, !- Program Line 1406 + elseif PMOT > ACSTaul, !- Program Line 1407 + set ACST = 26+ACSTtol, !- Program Line 1408 + endif, !- Program Line 1409 + endif, !- Program Line 1410 + endif, !- Program Line 1411 + if (ComfStand == 15) && (ComfMod == 2), !- Program Line 1412 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1413 + set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1414 + elseif CAT==80, !- Program Line 1415 + if PMOT < AHSTall, !- Program Line 1416 + set AHST = 19+AHSTtol, !- Program Line 1417 + elseif PMOT > AHSTaul, !- Program Line 1418 + set AHST = 22+AHSTtol, !- Program Line 1419 + endif, !- Program Line 1420 + elseif CAT==90, !- Program Line 1421 + if PMOT < AHSTall, !- Program Line 1422 + set AHST = 20+AHSTtol, !- Program Line 1423 + elseif PMOT > AHSTaul, !- Program Line 1424 + set AHST = 23+AHSTtol, !- Program Line 1425 + endif, !- Program Line 1426 + endif, !- Program Line 1427 + endif, !- Program Line 1428 + if (ComfStand == 15) && (ComfMod == 3), !- Program Line 1429 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1430 + set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1431 + elseif PMOT < ACSTall, !- Program Line 1432 + set ACST = ACSTall*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1433 + elseif PMOT > ACSTaul, !- Program Line 1434 + set ACST = ACSTaul*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1435 + endif, !- Program Line 1436 + endif, !- Program Line 1437 + if (ComfStand == 15) && (ComfMod == 3), !- Program Line 1438 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1439 + set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1440 + elseif PMOT < AHSTall, !- Program Line 1441 + set AHST = AHSTall*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1442 + elseif PMOT > AHSTaul, !- Program Line 1443 + set AHST = AHSTaul*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1444 + endif, !- Program Line 1445 + endif, !- Program Line 1446 + if (ComfStand == 16) && (ComfMod == 1), !- Program Line 1447 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1448 + set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1449 + elseif CAT==80, !- Program Line 1450 + if PMOT < ACSTall, !- Program Line 1451 + set ACST = 23.5+ACSTtol, !- Program Line 1452 + elseif PMOT > ACSTaul, !- Program Line 1453 + set ACST = 25.5+ACSTtol, !- Program Line 1454 + endif, !- Program Line 1455 + elseif CAT==90, !- Program Line 1456 + if PMOT < ACSTall, !- Program Line 1457 + set ACST = 23+ACSTtol, !- Program Line 1458 + elseif PMOT > ACSTaul, !- Program Line 1459 + set ACST = 25+ACSTtol, !- Program Line 1460 + endif, !- Program Line 1461 + endif, !- Program Line 1462 + endif, !- Program Line 1463 + if (ComfStand == 16) && (ComfMod == 1), !- Program Line 1464 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1465 + set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1466 + elseif CAT==80, !- Program Line 1467 + if PMOT < AHSTall, !- Program Line 1468 + set AHST = 21+AHSTtol, !- Program Line 1469 + elseif PMOT > AHSTaul, !- Program Line 1470 + set AHST = 22.5+AHSTtol, !- Program Line 1471 + endif, !- Program Line 1472 + elseif CAT==90, !- Program Line 1473 + if PMOT < AHSTall, !- Program Line 1474 + set AHST = 23+AHSTtol, !- Program Line 1475 + elseif PMOT > AHSTaul, !- Program Line 1476 + set AHST = 23+AHSTtol, !- Program Line 1477 + endif, !- Program Line 1478 + endif, !- Program Line 1479 + endif, !- Program Line 1480 + if (ComfStand == 16) && (ComfMod == 2), !- Program Line 1481 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1482 + set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1483 + elseif CAT==80, !- Program Line 1484 + if PMOT < ACSTall, !- Program Line 1485 + set ACST = 25+ACSTtol, !- Program Line 1486 + elseif PMOT > ACSTaul, !- Program Line 1487 + set ACST = 27+ACSTtol, !- Program Line 1488 + endif, !- Program Line 1489 + elseif CAT==90, !- Program Line 1490 + if PMOT < ACSTall, !- Program Line 1491 + set ACST = 24+ACSTtol, !- Program Line 1492 + elseif PMOT > ACSTaul, !- Program Line 1493 + set ACST = 26+ACSTtol, !- Program Line 1494 + endif, !- Program Line 1495 + endif, !- Program Line 1496 + endif, !- Program Line 1497 + if (ComfStand == 16) && (ComfMod == 2), !- Program Line 1498 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1499 + set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1500 + elseif CAT==80, !- Program Line 1501 + if PMOT < AHSTall, !- Program Line 1502 + set AHST = 19+AHSTtol, !- Program Line 1503 + elseif PMOT > AHSTaul, !- Program Line 1504 + set AHST = 22+AHSTtol, !- Program Line 1505 + endif, !- Program Line 1506 + elseif CAT==90, !- Program Line 1507 + if PMOT < AHSTall, !- Program Line 1508 + set AHST = 20+AHSTtol, !- Program Line 1509 + elseif PMOT > AHSTaul, !- Program Line 1510 + set AHST = 23+AHSTtol, !- Program Line 1511 + endif, !- Program Line 1512 + endif, !- Program Line 1513 + endif, !- Program Line 1514 + if (ComfStand == 16) && (ComfMod == 3), !- Program Line 1515 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1516 + set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1517 + elseif PMOT < ACSTall, !- Program Line 1518 + set ACST = ACSTall*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1519 + elseif PMOT > ACSTaul, !- Program Line 1520 + set ACST = ACSTaul*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1521 + endif, !- Program Line 1522 + endif, !- Program Line 1523 + if (ComfStand == 16) && (ComfMod == 3), !- Program Line 1524 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1525 + set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1526 + elseif PMOT < AHSTall, !- Program Line 1527 + set AHST = AHSTall*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1528 + elseif PMOT > AHSTaul, !- Program Line 1529 + set AHST = AHSTaul*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1530 + endif, !- Program Line 1531 + endif, !- Program Line 1532 + if (ComfStand == 17) || (ComfStand == 18) || (ComfStand == 19) || (ComfStand == 20), !- Program Line 1533 + if ComfMod == 0, !- Program Line 1534 + set ACST = 25+ACSTtol, !- Program Line 1535 + set AHST = 20+AHSTtol, !- Program Line 1536 + endif, !- Program Line 1537 + endif, !- Program Line 1538 + if (ComfStand == 17) && (ComfMod == 1), !- Program Line 1539 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1540 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1541 + set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1542 + else, !- Program Line 1543 + set ACST = 25+ACSTtol, !- Program Line 1544 + endif, !- Program Line 1545 + else, !- Program Line 1546 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1547 + set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1548 + else, !- Program Line 1549 + set ACST = 25+ACSTtol, !- Program Line 1550 + endif, !- Program Line 1551 + endif, !- Program Line 1552 + endif, !- Program Line 1553 + if (ComfStand == 17) && (ComfMod == 1), !- Program Line 1554 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1555 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1556 + set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1557 + else, !- Program Line 1558 + set AHST = 20+AHSTtol, !- Program Line 1559 + endif, !- Program Line 1560 + else, !- Program Line 1561 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1562 + set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1563 + else, !- Program Line 1564 + set AHST = 20+AHSTtol, !- Program Line 1565 + endif, !- Program Line 1566 + endif, !- Program Line 1567 + endif, !- Program Line 1568 + if (ComfStand == 17) && (ComfMod == 2), !- Program Line 1569 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1570 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1571 + set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1572 + elseif PMOT < ACSTall, !- Program Line 1573 + if CAT == 90, !- Program Line 1574 + set ACST = 24+ACSTtol, !- Program Line 1575 + elseif CAT == 80, !- Program Line 1576 + set ACST = 25+ACSTtol, !- Program Line 1577 + endif, !- Program Line 1578 + elseif PMOT > ACSTaul, !- Program Line 1579 + if CAT == 90, !- Program Line 1580 + set ACST = 26+ACSTtol, !- Program Line 1581 + elseif CAT == 80, !- Program Line 1582 + set ACST = 27+ACSTtol, !- Program Line 1583 + endif, !- Program Line 1584 + endif, !- Program Line 1585 + else, !- Program Line 1586 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1587 + set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1588 + elseif PMOT < ACSTall, !- Program Line 1589 + if CAT == 90, !- Program Line 1590 + set ACST = 24+ACSTtol, !- Program Line 1591 + elseif CAT == 80, !- Program Line 1592 + set ACST = 25+ACSTtol, !- Program Line 1593 + endif, !- Program Line 1594 + elseif PMOT > ACSTaul, !- Program Line 1595 + if CAT == 90, !- Program Line 1596 + set ACST = 26+ACSTtol, !- Program Line 1597 + elseif CAT == 80, !- Program Line 1598 + set ACST = 27+ACSTtol, !- Program Line 1599 + endif, !- Program Line 1600 + endif, !- Program Line 1601 + endif, !- Program Line 1602 + endif, !- Program Line 1603 + if (ComfStand == 17) && (ComfMod == 2), !- Program Line 1604 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1605 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1606 + set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1607 + elseif PMOT < AHSTall, !- Program Line 1608 + if CAT == 90, !- Program Line 1609 + set AHST = 20+AHSTtol, !- Program Line 1610 + elseif CAT == 80, !- Program Line 1611 + set AHST = 19+AHSTtol, !- Program Line 1612 + endif, !- Program Line 1613 + elseif PMOT > AHSTaul, !- Program Line 1614 + if CAT == 90, !- Program Line 1615 + set AHST = 23+AHSTtol, !- Program Line 1616 + elseif CAT == 80, !- Program Line 1617 + set AHST = 22+AHSTtol, !- Program Line 1618 + endif, !- Program Line 1619 + endif, !- Program Line 1620 + else, !- Program Line 1621 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1622 + set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1623 + elseif PMOT < AHSTall, !- Program Line 1624 + if CAT == 90, !- Program Line 1625 + set AHST = 20+AHSTtol, !- Program Line 1626 + elseif CAT == 80, !- Program Line 1627 + set AHST = 19+AHSTtol, !- Program Line 1628 + endif, !- Program Line 1629 + elseif PMOT > AHSTaul, !- Program Line 1630 + if CAT == 90, !- Program Line 1631 + set AHST = 23+AHSTtol, !- Program Line 1632 + elseif CAT == 80, !- Program Line 1633 + set AHST = 22+AHSTtol, !- Program Line 1634 + endif, !- Program Line 1635 + endif, !- Program Line 1636 + endif, !- Program Line 1637 + endif, !- Program Line 1638 + if (ComfStand == 17) && (ComfMod == 3), !- Program Line 1639 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1640 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1641 + set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1642 + elseif PMOT < ACSTall, !- Program Line 1643 + set ACST = ACSTall*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1644 + elseif PMOT > ACSTaul, !- Program Line 1645 + set ACST = ACSTaul*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1646 + endif, !- Program Line 1647 + else, !- Program Line 1648 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1649 + set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1650 + elseif PMOT < ACSTall, !- Program Line 1651 + set ACST = ACSTall*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1652 + elseif PMOT > ACSTaul, !- Program Line 1653 + set ACST = ACSTaul*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1654 + endif, !- Program Line 1655 + endif, !- Program Line 1656 + endif, !- Program Line 1657 + if (ComfStand == 17) && (ComfMod == 3), !- Program Line 1658 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1659 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1660 + set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1661 + elseif PMOT < AHSTall, !- Program Line 1662 + set AHST = AHSTall*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1663 + elseif PMOT > AHSTaul, !- Program Line 1664 + set AHST = AHSTaul*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1665 + endif, !- Program Line 1666 + else, !- Program Line 1667 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1668 + set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1669 + elseif PMOT < AHSTall, !- Program Line 1670 + set AHST = AHSTall*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1671 + elseif PMOT > AHSTaul, !- Program Line 1672 + set AHST = AHSTaul*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1673 + endif, !- Program Line 1674 + endif, !- Program Line 1675 + endif, !- Program Line 1676 + if (ComfStand == 18) && (ComfMod == 1), !- Program Line 1677 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1678 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1679 + set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1680 + else, !- Program Line 1681 + set ACST = 25+ACSTtol, !- Program Line 1682 + endif, !- Program Line 1683 + else, !- Program Line 1684 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1685 + set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1686 + else, !- Program Line 1687 + set ACST = 25+ACSTtol, !- Program Line 1688 + endif, !- Program Line 1689 + endif, !- Program Line 1690 + endif, !- Program Line 1691 + if (ComfStand == 18) && (ComfMod == 1), !- Program Line 1692 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1693 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1694 + set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1695 + else, !- Program Line 1696 + set AHST = 20+AHSTtol, !- Program Line 1697 + endif, !- Program Line 1698 + else, !- Program Line 1699 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1700 + set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1701 + else, !- Program Line 1702 + set AHST = 20+AHSTtol, !- Program Line 1703 + endif, !- Program Line 1704 + endif, !- Program Line 1705 + endif, !- Program Line 1706 + if (ComfStand == 18) && (ComfMod == 2), !- Program Line 1707 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1708 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1709 + set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1710 + elseif PMOT < ACSTall, !- Program Line 1711 + if CAT == 90, !- Program Line 1712 + set ACST = 24+ACSTtol, !- Program Line 1713 + elseif CAT == 80, !- Program Line 1714 + set ACST = 25+ACSTtol, !- Program Line 1715 + endif, !- Program Line 1716 + elseif PMOT > ACSTaul, !- Program Line 1717 + if CAT == 90, !- Program Line 1718 + set ACST = 26+ACSTtol, !- Program Line 1719 + elseif CAT == 80, !- Program Line 1720 + set ACST = 27+ACSTtol, !- Program Line 1721 + endif, !- Program Line 1722 + endif, !- Program Line 1723 + else, !- Program Line 1724 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1725 + set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1726 + elseif PMOT < ACSTall, !- Program Line 1727 + if CAT == 90, !- Program Line 1728 + set ACST = 24+ACSTtol, !- Program Line 1729 + elseif CAT == 80, !- Program Line 1730 + set ACST = 25+ACSTtol, !- Program Line 1731 + endif, !- Program Line 1732 + elseif PMOT > ACSTaul, !- Program Line 1733 + if CAT == 90, !- Program Line 1734 + set ACST = 26+ACSTtol, !- Program Line 1735 + elseif CAT == 80, !- Program Line 1736 + set ACST = 27+ACSTtol, !- Program Line 1737 + endif, !- Program Line 1738 + endif, !- Program Line 1739 + endif, !- Program Line 1740 + endif, !- Program Line 1741 + if (ComfStand == 18) && (ComfMod == 2), !- Program Line 1742 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1743 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1744 + set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1745 + elseif PMOT < AHSTall, !- Program Line 1746 + if CAT == 90, !- Program Line 1747 + set AHST = 20+AHSTtol, !- Program Line 1748 + elseif CAT == 80, !- Program Line 1749 + set AHST = 19+AHSTtol, !- Program Line 1750 + endif, !- Program Line 1751 + elseif PMOT > AHSTaul, !- Program Line 1752 + if CAT == 90, !- Program Line 1753 + set AHST = 23+AHSTtol, !- Program Line 1754 + elseif CAT == 80, !- Program Line 1755 + set AHST = 22+AHSTtol, !- Program Line 1756 + endif, !- Program Line 1757 + endif, !- Program Line 1758 + else, !- Program Line 1759 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1760 + set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1761 + elseif PMOT < AHSTall, !- Program Line 1762 + if CAT == 90, !- Program Line 1763 + set AHST = 20+AHSTtol, !- Program Line 1764 + elseif CAT == 80, !- Program Line 1765 + set AHST = 19+AHSTtol, !- Program Line 1766 + endif, !- Program Line 1767 + elseif PMOT > AHSTaul, !- Program Line 1768 + if CAT == 90, !- Program Line 1769 + set AHST = 23+AHSTtol, !- Program Line 1770 + elseif CAT == 80, !- Program Line 1771 + set AHST = 22+AHSTtol, !- Program Line 1772 + endif, !- Program Line 1773 + endif, !- Program Line 1774 + endif, !- Program Line 1775 + endif, !- Program Line 1776 + if (ComfStand == 18) && (ComfMod == 3), !- Program Line 1777 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1778 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1779 + set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1780 + elseif PMOT < ACSTall, !- Program Line 1781 + set ACST = ACSTall*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1782 + elseif PMOT > ACSTaul, !- Program Line 1783 + set ACST = ACSTaul*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1784 + endif, !- Program Line 1785 + else, !- Program Line 1786 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1787 + set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1788 + elseif PMOT < ACSTall, !- Program Line 1789 + set ACST = ACSTall*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1790 + elseif PMOT > ACSTaul, !- Program Line 1791 + set ACST = ACSTaul*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1792 + endif, !- Program Line 1793 + endif, !- Program Line 1794 + endif, !- Program Line 1795 + if (ComfStand == 18) && (ComfMod == 3), !- Program Line 1796 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1797 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1798 + set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1799 + elseif PMOT < AHSTall, !- Program Line 1800 + set AHST = AHSTall*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1801 + elseif PMOT > AHSTaul, !- Program Line 1802 + set AHST = AHSTaul*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1803 + endif, !- Program Line 1804 + else, !- Program Line 1805 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1806 + set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1807 + elseif PMOT < AHSTall, !- Program Line 1808 + set AHST = AHSTall*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1809 + elseif PMOT > AHSTaul, !- Program Line 1810 + set AHST = AHSTaul*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1811 + endif, !- Program Line 1812 + endif, !- Program Line 1813 + endif, !- Program Line 1814 + if (ComfStand == 19) && (ComfMod == 1), !- Program Line 1815 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1816 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1817 + set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1818 + else, !- Program Line 1819 + set ACST = 25+ACSTtol, !- Program Line 1820 + endif, !- Program Line 1821 + else, !- Program Line 1822 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1823 + set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1824 + else, !- Program Line 1825 + set ACST = 25+ACSTtol, !- Program Line 1826 + endif, !- Program Line 1827 + endif, !- Program Line 1828 + endif, !- Program Line 1829 + if (ComfStand == 19) && (ComfMod == 1), !- Program Line 1830 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1831 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1832 + set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1833 + else, !- Program Line 1834 + set AHST = 20+AHSTtol, !- Program Line 1835 + endif, !- Program Line 1836 + else, !- Program Line 1837 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1838 + set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1839 + else, !- Program Line 1840 + set AHST = 20+AHSTtol, !- Program Line 1841 + endif, !- Program Line 1842 + endif, !- Program Line 1843 + endif, !- Program Line 1844 + if (ComfStand == 19) && (ComfMod == 2), !- Program Line 1845 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1846 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1847 + set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1848 + elseif PMOT < ACSTall, !- Program Line 1849 + if CAT == 90, !- Program Line 1850 + set ACST = 24+ACSTtol, !- Program Line 1851 + elseif CAT == 80, !- Program Line 1852 + set ACST = 25+ACSTtol, !- Program Line 1853 + endif, !- Program Line 1854 + elseif PMOT > ACSTaul, !- Program Line 1855 + if CAT == 90, !- Program Line 1856 + set ACST = 26+ACSTtol, !- Program Line 1857 + elseif CAT == 80, !- Program Line 1858 + set ACST = 27+ACSTtol, !- Program Line 1859 + endif, !- Program Line 1860 + endif, !- Program Line 1861 + else, !- Program Line 1862 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1863 + set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1864 + elseif PMOT < ACSTall, !- Program Line 1865 + if CAT == 90, !- Program Line 1866 + set ACST = 24+ACSTtol, !- Program Line 1867 + elseif CAT == 80, !- Program Line 1868 + set ACST = 25+ACSTtol, !- Program Line 1869 + endif, !- Program Line 1870 + elseif PMOT > ACSTaul, !- Program Line 1871 + if CAT == 90, !- Program Line 1872 + set ACST = 26+ACSTtol, !- Program Line 1873 + elseif CAT == 80, !- Program Line 1874 + set ACST = 27+ACSTtol, !- Program Line 1875 + endif, !- Program Line 1876 + endif, !- Program Line 1877 + endif, !- Program Line 1878 + endif, !- Program Line 1879 + if (ComfStand == 19) && (ComfMod == 2), !- Program Line 1880 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1881 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1882 + set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1883 + elseif PMOT < AHSTall, !- Program Line 1884 + if CAT == 90, !- Program Line 1885 + set AHST = 20+AHSTtol, !- Program Line 1886 + elseif CAT == 80, !- Program Line 1887 + set AHST = 19+AHSTtol, !- Program Line 1888 + endif, !- Program Line 1889 + elseif PMOT > AHSTaul, !- Program Line 1890 + if CAT == 90, !- Program Line 1891 + set AHST = 23+AHSTtol, !- Program Line 1892 + elseif CAT == 80, !- Program Line 1893 + set AHST = 22+AHSTtol, !- Program Line 1894 + endif, !- Program Line 1895 + endif, !- Program Line 1896 + else, !- Program Line 1897 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1898 + set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1899 + elseif PMOT < AHSTall, !- Program Line 1900 + if CAT == 90, !- Program Line 1901 + set AHST = 20+AHSTtol, !- Program Line 1902 + elseif CAT == 80, !- Program Line 1903 + set AHST = 19+AHSTtol, !- Program Line 1904 + endif, !- Program Line 1905 + elseif PMOT > AHSTaul, !- Program Line 1906 + if CAT == 90, !- Program Line 1907 + set AHST = 23+AHSTtol, !- Program Line 1908 + elseif CAT == 80, !- Program Line 1909 + set AHST = 22+AHSTtol, !- Program Line 1910 + endif, !- Program Line 1911 + endif, !- Program Line 1912 + endif, !- Program Line 1913 + endif, !- Program Line 1914 + if (ComfStand == 19) && (ComfMod == 3), !- Program Line 1915 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1916 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1917 + set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1918 + elseif PMOT < ACSTall, !- Program Line 1919 + set ACST = ACSTall*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1920 + elseif PMOT > ACSTaul, !- Program Line 1921 + set ACST = ACSTaul*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1922 + endif, !- Program Line 1923 + else, !- Program Line 1924 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1925 + set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1926 + elseif PMOT < ACSTall, !- Program Line 1927 + set ACST = ACSTall*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1928 + elseif PMOT > ACSTaul, !- Program Line 1929 + set ACST = ACSTaul*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1930 + endif, !- Program Line 1931 + endif, !- Program Line 1932 + endif, !- Program Line 1933 + if (ComfStand == 19) && (ComfMod == 3), !- Program Line 1934 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1935 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1936 + set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1937 + elseif PMOT < AHSTall, !- Program Line 1938 + set AHST = AHSTall*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1939 + elseif PMOT > AHSTaul, !- Program Line 1940 + set AHST = AHSTaul*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1941 + endif, !- Program Line 1942 + else, !- Program Line 1943 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1944 + set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1945 + elseif PMOT < AHSTall, !- Program Line 1946 + set AHST = AHSTall*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1947 + elseif PMOT > AHSTaul, !- Program Line 1948 + set AHST = AHSTaul*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1949 + endif, !- Program Line 1950 + endif, !- Program Line 1951 + endif, !- Program Line 1952 + if (ComfStand == 20) && (ComfMod == 1), !- Program Line 1953 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1954 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1955 + set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 1956 + else, !- Program Line 1957 + set ACST = 25+ACSTtol, !- Program Line 1958 + endif, !- Program Line 1959 + else, !- Program Line 1960 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1961 + set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 1962 + else, !- Program Line 1963 + set ACST = 25+ACSTtol, !- Program Line 1964 + endif, !- Program Line 1965 + endif, !- Program Line 1966 + endif, !- Program Line 1967 + if (ComfStand == 20) && (ComfMod == 1), !- Program Line 1968 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1969 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1970 + set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 1971 + else, !- Program Line 1972 + set AHST = 20+AHSTtol, !- Program Line 1973 + endif, !- Program Line 1974 + else, !- Program Line 1975 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1976 + set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 1977 + else, !- Program Line 1978 + set AHST = 20+AHSTtol, !- Program Line 1979 + endif, !- Program Line 1980 + endif, !- Program Line 1981 + endif, !- Program Line 1982 + if (ComfStand == 20) && (ComfMod == 2), !- Program Line 1983 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1984 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1985 + set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 1986 + elseif PMOT < ACSTall, !- Program Line 1987 + if CAT == 90, !- Program Line 1988 + set ACST = 24+ACSTtol, !- Program Line 1989 + elseif CAT == 80, !- Program Line 1990 + set ACST = 25+ACSTtol, !- Program Line 1991 + endif, !- Program Line 1992 + elseif PMOT > ACSTaul, !- Program Line 1993 + if CAT == 90, !- Program Line 1994 + set ACST = 26+ACSTtol, !- Program Line 1995 + elseif CAT == 80, !- Program Line 1996 + set ACST = 27+ACSTtol, !- Program Line 1997 + endif, !- Program Line 1998 + endif, !- Program Line 1999 + else, !- Program Line 2000 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2001 + set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2002 + elseif PMOT < ACSTall, !- Program Line 2003 + if CAT == 90, !- Program Line 2004 + set ACST = 24+ACSTtol, !- Program Line 2005 + elseif CAT == 80, !- Program Line 2006 + set ACST = 25+ACSTtol, !- Program Line 2007 + endif, !- Program Line 2008 + elseif PMOT > ACSTaul, !- Program Line 2009 + if CAT == 90, !- Program Line 2010 + set ACST = 26+ACSTtol, !- Program Line 2011 + elseif CAT == 80, !- Program Line 2012 + set ACST = 27+ACSTtol, !- Program Line 2013 + endif, !- Program Line 2014 + endif, !- Program Line 2015 + endif, !- Program Line 2016 + endif, !- Program Line 2017 + if (ComfStand == 20) && (ComfMod == 2), !- Program Line 2018 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2019 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2020 + set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2021 + elseif PMOT < AHSTall, !- Program Line 2022 + if CAT == 90, !- Program Line 2023 + set AHST = 20+AHSTtol, !- Program Line 2024 + elseif CAT == 80, !- Program Line 2025 + set AHST = 19+AHSTtol, !- Program Line 2026 + endif, !- Program Line 2027 + elseif PMOT > AHSTaul, !- Program Line 2028 + if CAT == 90, !- Program Line 2029 + set AHST = 23+AHSTtol, !- Program Line 2030 + elseif CAT == 80, !- Program Line 2031 + set AHST = 22+AHSTtol, !- Program Line 2032 + endif, !- Program Line 2033 + endif, !- Program Line 2034 + else, !- Program Line 2035 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2036 + set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2037 + elseif PMOT < AHSTall, !- Program Line 2038 + if CAT == 90, !- Program Line 2039 + set AHST = 20+AHSTtol, !- Program Line 2040 + elseif CAT == 80, !- Program Line 2041 + set AHST = 19+AHSTtol, !- Program Line 2042 + endif, !- Program Line 2043 + elseif PMOT > AHSTaul, !- Program Line 2044 + if CAT == 90, !- Program Line 2045 + set AHST = 23+AHSTtol, !- Program Line 2046 + elseif CAT == 80, !- Program Line 2047 + set AHST = 22+AHSTtol, !- Program Line 2048 + endif, !- Program Line 2049 + endif, !- Program Line 2050 + endif, !- Program Line 2051 + endif, !- Program Line 2052 + if (ComfStand == 20) && (ComfMod == 3), !- Program Line 2053 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2054 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2055 + set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2056 + elseif PMOT < ACSTall, !- Program Line 2057 + set ACST = ACSTall*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2058 + elseif PMOT > ACSTaul, !- Program Line 2059 + set ACST = ACSTaul*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2060 + endif, !- Program Line 2061 + else, !- Program Line 2062 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2063 + set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2064 + elseif PMOT < ACSTall, !- Program Line 2065 + set ACST = ACSTall*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2066 + elseif PMOT > ACSTaul, !- Program Line 2067 + set ACST = ACSTaul*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2068 + endif, !- Program Line 2069 + endif, !- Program Line 2070 + endif, !- Program Line 2071 + if (ComfStand == 20) && (ComfMod == 3), !- Program Line 2072 + if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2073 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2074 + set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2075 + elseif PMOT < AHSTall, !- Program Line 2076 + set AHST = AHSTall*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2077 + elseif PMOT > AHSTaul, !- Program Line 2078 + set AHST = AHSTaul*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2079 + endif, !- Program Line 2080 + else, !- Program Line 2081 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2082 + set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2083 + elseif PMOT < AHSTall, !- Program Line 2084 + set AHST = AHSTall*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2085 + elseif PMOT > AHSTaul, !- Program Line 2086 + set AHST = AHSTaul*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2087 + endif, !- Program Line 2088 + endif, !- Program Line 2089 + endif, !- Program Line 2090 + if (ComfStand == 21) && (ComfMod == 2), !- Program Line 2091 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2092 + set ACST = PMOT*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2093 + elseif CAT==80, !- Program Line 2094 + if PMOT < ACSTall, !- Program Line 2095 + set ACST = 25+ACSTtol, !- Program Line 2096 + elseif PMOT > ACSTaul, !- Program Line 2097 + set ACST = 27+ACSTtol, !- Program Line 2098 + endif, !- Program Line 2099 + elseif CAT==90, !- Program Line 2100 + if PMOT < ACSTall, !- Program Line 2101 + set ACST = 24+ACSTtol, !- Program Line 2102 + elseif PMOT > ACSTaul, !- Program Line 2103 + set ACST = 26+ACSTtol, !- Program Line 2104 + endif, !- Program Line 2105 + endif, !- Program Line 2106 + endif, !- Program Line 2107 + if (ComfStand == 21) && (ComfMod == 2), !- Program Line 2108 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2109 + set AHST = PMOT*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2110 + elseif CAT==80, !- Program Line 2111 + if PMOT < AHSTall, !- Program Line 2112 + set AHST = 19+AHSTtol, !- Program Line 2113 + elseif PMOT > AHSTaul, !- Program Line 2114 + set AHST = 22+AHSTtol, !- Program Line 2115 + endif, !- Program Line 2116 + elseif CAT==90, !- Program Line 2117 + if PMOT < AHSTall, !- Program Line 2118 + set AHST = 20+AHSTtol, !- Program Line 2119 + elseif PMOT > AHSTaul, !- Program Line 2120 + set AHST = 23+AHSTtol, !- Program Line 2121 + endif, !- Program Line 2122 + endif, !- Program Line 2123 + endif, !- Program Line 2124 + if (ComfStand == 21) && (ComfMod == 3), !- Program Line 2125 + if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2126 + set ACST = PMOT*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2127 + elseif PMOT < ACSTall, !- Program Line 2128 + set ACST = ACSTall*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2129 + elseif PMOT > ACSTaul, !- Program Line 2130 + set ACST = ACSTaul*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2131 + endif, !- Program Line 2132 + endif, !- Program Line 2133 + if (ComfStand == 21) && (ComfMod == 3), !- Program Line 2134 + if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2135 + set AHST = PMOT*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2136 + elseif PMOT < AHSTall, !- Program Line 2137 + set AHST = AHSTall*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2138 + elseif PMOT > AHSTaul, !- Program Line 2139 + set AHST = AHSTaul*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2140 + endif, !- Program Line 2141 + endif, !- Program Line 2142 + if (ComfStand == 22), !- Program Line 2143 + if CoolingSeason == 1, !- Program Line 2144 + if (CAT==3), !- Program Line 2145 + set ACST = 24.5+2.5+ACSTtol, !- Program Line 2146 + elseif (CAT==2), !- Program Line 2147 + set ACST = 24.5+1.5+ACSTtol, !- Program Line 2148 + elseif (CAT==1), !- Program Line 2149 + set ACST = 24.5+1+ACSTtol, !- Program Line 2150 + endif, !- Program Line 2151 + else, !- Program Line 2152 + if (CAT==3), !- Program Line 2153 + set ACST = 22+3+ACSTtol, !- Program Line 2154 + elseif (CAT==2), !- Program Line 2155 + set ACST = 22+2+ACSTtol, !- Program Line 2156 + elseif (CAT==1), !- Program Line 2157 + set ACST = 22+1+ACSTtol, !- Program Line 2158 + endif, !- Program Line 2159 + endif, !- Program Line 2160 + endif, !- Program Line 2161 + if (ComfStand == 22), !- Program Line 2162 + if CoolingSeason == 1, !- Program Line 2163 + if (CAT==3), !- Program Line 2164 + set AHST = 24.5-2.5+AHSTtol, !- Program Line 2165 + elseif (CAT==2), !- Program Line 2166 + set AHST = 24.5-1.5+AHSTtol, !- Program Line 2167 + elseif (CAT==1), !- Program Line 2168 + set AHST = 24.5-1+AHSTtol, !- Program Line 2169 + endif, !- Program Line 2170 + else, !- Program Line 2171 + if (CAT==3), !- Program Line 2172 + set AHST = 22-3+AHSTtol, !- Program Line 2173 + elseif (CAT==2), !- Program Line 2174 + set AHST = 22-2+AHSTtol, !- Program Line 2175 + elseif (CAT==1), !- Program Line 2176 + set AHST = 22-1+AHSTtol, !- Program Line 2177 + endif, !- Program Line 2178 + endif, !- Program Line 2179 + endif, !- Program Line 2180 + set ACSTx2 = ACST*SetpointAcc, !- Program Line 2181 + set AHSTx2 = AHST*SetpointAcc, !- Program Line 2182 + set roundedACSTx2 = @Round ACSTx2, !- Program Line 2183 + set roundedAHSTx2 = @Round AHSTx2, !- Program Line 2184 + if roundedACSTx2 - ACSTx2 < 0, !- Program Line 2185 + set ACSTroundedUp = 0, !- Program Line 2186 + else, !- Program Line 2187 + set ACSTroundedUp = 1, !- Program Line 2188 + endif, !- Program Line 2189 + if roundedAHSTx2 - AHSTx2 < 0, !- Program Line 2190 + set AHSTroundedUp = 0, !- Program Line 2191 + else, !- Program Line 2192 + set AHSTroundedUp = 1, !- Program Line 2193 + endif, !- Program Line 2194 + if ACSTroundedUp == 0, !- Program Line 2195 + set roundedACST = roundedACSTx2 / SetpointAcc, !- Program Line 2196 + else, !- Program Line 2197 + set roundedACST = (roundedACSTx2 - 1) / SetpointAcc, !- Program Line 2198 + endif, !- Program Line 2199 + if AHSTroundedUp == 0, !- Program Line 2200 + set roundedAHST = (roundedAHSTx2 + 1) / SetpointAcc, !- Program Line 2201 + else, !- Program Line 2202 + set roundedAHST = roundedAHSTx2 / SetpointAcc, !- Program Line 2203 + endif; !- Program Line 2204 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetASTnoTol, !- Name + set ACSTnoTol = ACST-ACSTtol, !- Program Line 1 + set AHSTnoTol = AHST-AHSTtol; !- Program Line 2 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + CountHoursNoApp_BLOCK1_ZONE2, !- Name + if (BLOCK1_ZONE2_OpT <= ACSTnoTol), !- Program Line 1 + if (BLOCK1_ZONE2_OpT >= AHSTnoTol), !- Program Line 2 + set ComfHoursNoApp_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 3 + if Occ_count_BLOCK1_ZONE2, !- Program Line 4 + set OccComfHoursNoApp_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 5 + else, !- Program Line 6 + set OccComfHoursNoApp_BLOCK1_ZONE2 = 0, !- Program Line 7 + endif, !- Program Line 8 + else, !- Program Line 9 + set ComfHoursNoApp_BLOCK1_ZONE2 = 0, !- Program Line 10 + endif, !- Program Line 11 + else, !- Program Line 12 + set ComfHoursNoApp_BLOCK1_ZONE2 = 0, !- Program Line 13 + endif, !- Program Line 14 + if BLOCK1_ZONE2_OpT > ACSTnoTol || BLOCK1_ZONE2_OpT < AHSTnoTol, !- Program Line 15 + if Occ_count_BLOCK1_ZONE2, !- Program Line 16 + set OccDiscomfHoursNoApp_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 17 + else, !- Program Line 18 + set OccDiscomfHoursNoApp_BLOCK1_ZONE2 = 0, !- Program Line 19 + endif, !- Program Line 20 + else, !- Program Line 21 + set OccDiscomfHoursNoApp_BLOCK1_ZONE2 = 0, !- Program Line 22 + endif, !- Program Line 23 + if Occ_count_BLOCK1_ZONE2 > 0, !- Program Line 24 + set OccHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 25 + else, !- Program Line 26 + set OccHours_BLOCK1_ZONE2 = 0, !- Program Line 27 + endif; !- Program Line 28 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetGeoVarBLOCK1_ZONE2, !- Name + set ZoneFloorArea_BLOCK1_ZONE2 = ZFA_BLOCK1_ZONE2/2, !- Program Line 1 + set ZoneAirVolume_BLOCK1_ZONE2 = ZAV_BLOCK1_ZONE2/2; !- Program Line 2 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + CountHoursNoApp_BLOCK1_ZONE1, !- Name + if (BLOCK1_ZONE1_OpT <= ACSTnoTol), !- Program Line 1 + if (BLOCK1_ZONE1_OpT >= AHSTnoTol), !- Program Line 2 + set ComfHoursNoApp_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 3 + if Occ_count_BLOCK1_ZONE1, !- Program Line 4 + set OccComfHoursNoApp_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 5 + else, !- Program Line 6 + set OccComfHoursNoApp_BLOCK1_ZONE1 = 0, !- Program Line 7 + endif, !- Program Line 8 + else, !- Program Line 9 + set ComfHoursNoApp_BLOCK1_ZONE1 = 0, !- Program Line 10 + endif, !- Program Line 11 + else, !- Program Line 12 + set ComfHoursNoApp_BLOCK1_ZONE1 = 0, !- Program Line 13 + endif, !- Program Line 14 + if BLOCK1_ZONE1_OpT > ACSTnoTol || BLOCK1_ZONE1_OpT < AHSTnoTol, !- Program Line 15 + if Occ_count_BLOCK1_ZONE1, !- Program Line 16 + set OccDiscomfHoursNoApp_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 17 + else, !- Program Line 18 + set OccDiscomfHoursNoApp_BLOCK1_ZONE1 = 0, !- Program Line 19 + endif, !- Program Line 20 + else, !- Program Line 21 + set OccDiscomfHoursNoApp_BLOCK1_ZONE1 = 0, !- Program Line 22 + endif, !- Program Line 23 + if Occ_count_BLOCK1_ZONE1 > 0, !- Program Line 24 + set OccHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 25 + else, !- Program Line 26 + set OccHours_BLOCK1_ZONE1 = 0, !- Program Line 27 + endif; !- Program Line 28 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetGeoVarBLOCK1_ZONE1, !- Name + set ZoneFloorArea_BLOCK1_ZONE1 = ZFA_BLOCK1_ZONE1/2, !- Program Line 1 + set ZoneAirVolume_BLOCK1_ZONE1 = ZAV_BLOCK1_ZONE1/2; !- Program Line 2 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetInputData, !- Name + set ComfStand = 1, !- Program Line 1 + set CAT = 1, !- Program Line 2 + set ComfMod = 2, !- Program Line 3 + set HVACmode = 2, !- Program Line 4 + set VentCtrl = 0, !- Program Line 5 + set VSToffset = 0, !- Program Line 6 + set MinOToffset = 7, !- Program Line 7 + set MaxWindSpeed = 6, !- Program Line 8 + set ACSTtol = -0.25, !- Program Line 9 + set AHSTtol = 0.25, !- Program Line 10 + set CoolSeasonStart = 121, !- Program Line 11 + set CoolSeasonEnd = 274; !- Program Line 12 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetVOFinputData, !- Name + set MaxTempDiffVOF = 7.5, !- Program Line 1 + set MinTempDiffVOF = 0, !- Program Line 2 + set MultiplierVOF = 0.25; !- Program Line 3 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetVST, !- Name + set MinOutTemp = roundedAHST - MinOToffset, !- Program Line 1 + if (VentCtrl == 0) || (VentCtrl==2), !- Program Line 2 + if ComfStand == 0, !- Program Line 3 + if (CurrentTime < 7), !- Program Line 4 + set VST = (roundedACST+roundedAHST)/2+VSToffset, !- Program Line 5 + elseif (CurrentTime < 15), !- Program Line 6 + set VST = 22.5+VSToffset, !- Program Line 7 + elseif (CurrentTime < 23), !- Program Line 8 + set VST = (roundedACST+roundedAHST)/2+VSToffset, !- Program Line 9 + elseif (CurrentTime < 24), !- Program Line 10 + set VST = (roundedACST+roundedAHST)/2+VSToffset, !- Program Line 11 + endif, !- Program Line 12 + elseif ComfStand == 1 || ComfStand == 10, !- Program Line 13 + if (RMOT >= AHSTall) && (RMOT <= ACSTaul), !- Program Line 14 + set VST = ComfTemp+VSToffset, !- Program Line 15 + else, !- Program Line 16 + set VST = (roundedACST+roundedAHST)/2+VSToffset, !- Program Line 17 + endif, !- Program Line 18 + elseif ComfStand == 4 || ComfStand == 5 || ComfStand == 6, !- Program Line 19 + if (PMOT >= AHSTall) && (PMOT <= ACSTaul), !- Program Line 20 + set VST = (roundedACST+roundedAHST)/2+VSToffset, !- Program Line 21 + else, !- Program Line 22 + set VST = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + if (PMOT >= AHSTall) && (PMOT <= ACSTaul), !- Program Line 26 + set VST = ComfTemp+VSToffset, !- Program Line 27 + else, !- Program Line 28 + set VST = (roundedACST+roundedAHST)/2+VSToffset, !- Program Line 29 + endif, !- Program Line 30 + endif, !- Program Line 31 + elseif (VentCtrl == 1) || (VentCtrl==3), !- Program Line 32 + set VST = roundedAHST+VSToffset, !- Program Line 33 + endif; !- Program Line 34 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + ApplyAST_BLOCK1_ZONE2, !- Name + if BLOCK1_ZONE2_CoolCoil==0, !- Program Line 1 + if BLOCK1_ZONE2_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if BLOCK1_ZONE2_OpTMinOutTemp, !- Program Line 12 + if BLOCK1_ZONE2_OutT < BLOCK1_ZONE2_OpT, !- Program Line 13 + set meets_base_reqs = 1, !- Program Line 14 + else, !- Program Line 15 + set meets_base_reqs = 0, !- Program Line 16 + endif, !- Program Line 17 + else, !- Program Line 18 + set meets_base_reqs = 0, !- Program Line 19 + endif, !- Program Line 20 + else, !- Program Line 21 + set meets_base_reqs = 0, !- Program Line 22 + endif, !- Program Line 23 + else, !- Program Line 24 + set meets_base_reqs = 0, !- Program Line 25 + endif, !- Program Line 26 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 27 + if BLOCK1_ZONE2_OpT>VST, !- Program Line 28 + set Ventilates_HVACmode2_BLOCK1_ZONE2 = 1, !- Program Line 29 + else, !- Program Line 30 + set Ventilates_HVACmode2_BLOCK1_ZONE2 = 0, !- Program Line 31 + endif, !- Program Line 32 + else, !- Program Line 33 + set Ventilates_HVACmode2_BLOCK1_ZONE2 = 0, !- Program Line 34 + endif, !- Program Line 35 + if VentCtrl == 0, !- Program Line 36 + if BLOCK1_ZONE2_OutT < BLOCK1_ZONE2_OpT, !- Program Line 37 + if BLOCK1_ZONE2_OutT>MinOutTemp, !- Program Line 38 + if BLOCK1_ZONE2_OpT > VST, !- Program Line 39 + if BLOCK1_ZONE2_WindSpeed <= MaxWindSpeed, !- Program Line 40 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 1, !- Program Line 41 + else, !- Program Line 42 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 43 + endif, !- Program Line 44 + else, !- Program Line 45 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 46 + endif, !- Program Line 47 + else, !- Program Line 48 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 49 + endif, !- Program Line 50 + else, !- Program Line 51 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 52 + endif, !- Program Line 53 + elseif VentCtrl == 1, !- Program Line 54 + if BLOCK1_ZONE2_OutTMinOutTemp, !- Program Line 56 + if BLOCK1_ZONE2_OpT > ACSTnoTol, !- Program Line 57 + if BLOCK1_ZONE2_WindSpeed <= MaxWindSpeed, !- Program Line 58 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 1, !- Program Line 59 + else, !- Program Line 60 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 61 + endif, !- Program Line 62 + else, !- Program Line 63 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 64 + endif, !- Program Line 65 + else, !- Program Line 66 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 67 + endif, !- Program Line 68 + else, !- Program Line 69 + set Ventilates_HVACmode1_BLOCK1_ZONE2 = 0, !- Program Line 70 + endif, !- Program Line 71 + endif, !- Program Line 72 + if HVACmode == 0, !- Program Line 73 + if Occ_count_BLOCK1_ZONE2 == 0, !- Program Line 74 + Set ACST_Act_BLOCK1_ZONE2 = 100, !- Program Line 75 + Set AHST_Act_BLOCK1_ZONE2 = -100, !- Program Line 76 + else, !- Program Line 77 + set ACST_Act_BLOCK1_ZONE2 = roundedACST, !- Program Line 78 + set AHST_Act_BLOCK1_ZONE2 = roundedAHST, !- Program Line 79 + endif, !- Program Line 80 + elseif HVACmode == 1, !- Program Line 81 + Set ACST_Act_BLOCK1_ZONE2 = 100, !- Program Line 82 + Set AHST_Act_BLOCK1_ZONE2 = -100, !- Program Line 83 + if Ventilates_HVACmode1_BLOCK1_ZONE2 == 1 && Occ_count_BLOCK1_ZONE2 > 0, !- Program Line 84 + set VentHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 85 + else, !- Program Line 86 + set VentHours_BLOCK1_ZONE2 = 0, !- Program Line 87 + endif, !- Program Line 88 + elseif HVACmode == 2, !- Program Line 89 + if Occ_count_BLOCK1_ZONE2 == 0, !- Program Line 90 + Set ACST_Act_BLOCK1_ZONE2 = 100, !- Program Line 91 + Set AHST_Act_BLOCK1_ZONE2 = -100, !- Program Line 92 + set VentHours_BLOCK1_ZONE2 = 0, !- Program Line 93 + else, !- Program Line 94 + if Ventilates_HVACmode2_BLOCK1_ZONE2 == 1, !- Program Line 95 + set VentHours_BLOCK1_ZONE2 = 1*ZoneTimeStep, !- Program Line 96 + elseif Ventilates_HVACmode2_BLOCK1_ZONE2 == 0, !- Program Line 97 + set VentHours_BLOCK1_ZONE2 = 0, !- Program Line 98 + set ACST_Act_BLOCK1_ZONE2 = roundedACST, !- Program Line 99 + set AHST_Act_BLOCK1_ZONE2 = roundedAHST, !- Program Line 100 + endif, !- Program Line 101 + endif, !- Program Line 102 + endif; !- Program Line 103 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + ApplyAST_BLOCK1_ZONE1, !- Name + if BLOCK1_ZONE1_CoolCoil==0, !- Program Line 1 + if BLOCK1_ZONE1_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if BLOCK1_ZONE1_OpTMinOutTemp, !- Program Line 12 + if BLOCK1_ZONE1_OutT < BLOCK1_ZONE1_OpT, !- Program Line 13 + set meets_base_reqs = 1, !- Program Line 14 + else, !- Program Line 15 + set meets_base_reqs = 0, !- Program Line 16 + endif, !- Program Line 17 + else, !- Program Line 18 + set meets_base_reqs = 0, !- Program Line 19 + endif, !- Program Line 20 + else, !- Program Line 21 + set meets_base_reqs = 0, !- Program Line 22 + endif, !- Program Line 23 + else, !- Program Line 24 + set meets_base_reqs = 0, !- Program Line 25 + endif, !- Program Line 26 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 27 + if BLOCK1_ZONE1_OpT>VST, !- Program Line 28 + set Ventilates_HVACmode2_BLOCK1_ZONE1 = 1, !- Program Line 29 + else, !- Program Line 30 + set Ventilates_HVACmode2_BLOCK1_ZONE1 = 0, !- Program Line 31 + endif, !- Program Line 32 + else, !- Program Line 33 + set Ventilates_HVACmode2_BLOCK1_ZONE1 = 0, !- Program Line 34 + endif, !- Program Line 35 + if VentCtrl == 0, !- Program Line 36 + if BLOCK1_ZONE1_OutT < BLOCK1_ZONE1_OpT, !- Program Line 37 + if BLOCK1_ZONE1_OutT>MinOutTemp, !- Program Line 38 + if BLOCK1_ZONE1_OpT > VST, !- Program Line 39 + if BLOCK1_ZONE1_WindSpeed <= MaxWindSpeed, !- Program Line 40 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 1, !- Program Line 41 + else, !- Program Line 42 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 43 + endif, !- Program Line 44 + else, !- Program Line 45 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 46 + endif, !- Program Line 47 + else, !- Program Line 48 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 49 + endif, !- Program Line 50 + else, !- Program Line 51 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 52 + endif, !- Program Line 53 + elseif VentCtrl == 1, !- Program Line 54 + if BLOCK1_ZONE1_OutTMinOutTemp, !- Program Line 56 + if BLOCK1_ZONE1_OpT > ACSTnoTol, !- Program Line 57 + if BLOCK1_ZONE1_WindSpeed <= MaxWindSpeed, !- Program Line 58 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 1, !- Program Line 59 + else, !- Program Line 60 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 61 + endif, !- Program Line 62 + else, !- Program Line 63 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 64 + endif, !- Program Line 65 + else, !- Program Line 66 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 67 + endif, !- Program Line 68 + else, !- Program Line 69 + set Ventilates_HVACmode1_BLOCK1_ZONE1 = 0, !- Program Line 70 + endif, !- Program Line 71 + endif, !- Program Line 72 + if HVACmode == 0, !- Program Line 73 + if Occ_count_BLOCK1_ZONE1 == 0, !- Program Line 74 + Set ACST_Act_BLOCK1_ZONE1 = 100, !- Program Line 75 + Set AHST_Act_BLOCK1_ZONE1 = -100, !- Program Line 76 + else, !- Program Line 77 + set ACST_Act_BLOCK1_ZONE1 = roundedACST, !- Program Line 78 + set AHST_Act_BLOCK1_ZONE1 = roundedAHST, !- Program Line 79 + endif, !- Program Line 80 + elseif HVACmode == 1, !- Program Line 81 + Set ACST_Act_BLOCK1_ZONE1 = 100, !- Program Line 82 + Set AHST_Act_BLOCK1_ZONE1 = -100, !- Program Line 83 + if Ventilates_HVACmode1_BLOCK1_ZONE1 == 1 && Occ_count_BLOCK1_ZONE1 > 0, !- Program Line 84 + set VentHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 85 + else, !- Program Line 86 + set VentHours_BLOCK1_ZONE1 = 0, !- Program Line 87 + endif, !- Program Line 88 + elseif HVACmode == 2, !- Program Line 89 + if Occ_count_BLOCK1_ZONE1 == 0, !- Program Line 90 + Set ACST_Act_BLOCK1_ZONE1 = 100, !- Program Line 91 + Set AHST_Act_BLOCK1_ZONE1 = -100, !- Program Line 92 + set VentHours_BLOCK1_ZONE1 = 0, !- Program Line 93 + else, !- Program Line 94 + if Ventilates_HVACmode2_BLOCK1_ZONE1 == 1, !- Program Line 95 + set VentHours_BLOCK1_ZONE1 = 1*ZoneTimeStep, !- Program Line 96 + elseif Ventilates_HVACmode2_BLOCK1_ZONE1 == 0, !- Program Line 97 + set VentHours_BLOCK1_ZONE1 = 0, !- Program Line 98 + set ACST_Act_BLOCK1_ZONE1 = roundedACST, !- Program Line 99 + set AHST_Act_BLOCK1_ZONE1 = roundedAHST, !- Program Line 100 + endif, !- Program Line 101 + endif, !- Program Line 102 + endif; !- Program Line 103 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win, !- Name + set slope = (1 - MultiplierVOF) / (MinTempDiffVOF - MaxTempDiffVOF), !- Program Line 1 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT > 0, !- Program Line 2 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT, !- Program Line 3 + else, !- Program Line 4 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT + Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT, !- Program Line 5 + endif, !- Program Line 6 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT > Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT, !- Program Line 7 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF = 0, !- Program Line 8 + else, !- Program Line 9 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_TempDiffVOF > MaxTempDiffVOF, !- Program Line 10 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF = MultiplierVOF, !- Program Line 11 + elseif Block1_Zone2_Wall_3_0_0_0_0_0_Win_TempDiffVOF < MinTempDiffVOF, !- Program Line 12 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF = 1.0, !- Program Line 13 + else, !- Program Line 14 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF = slope*Block1_Zone2_Wall_3_0_0_0_0_0_Win_TempDiffVOF - slope*MinTempDiffVOF + 1, !- Program Line 15 + endif, !- Program Line 16 + endif; !- Program Line 17 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win, !- Name + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil==0, !- Program Line 1 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpTMinOutTemp, !- Program Line 12 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT < Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT, !- Program Line 13 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count > 0, !- Program Line 14 + set meets_base_reqs = 1, !- Program Line 15 + else, !- Program Line 16 + set meets_base_reqs = 0, !- Program Line 17 + endif, !- Program Line 18 + else, !- Program Line 19 + set meets_base_reqs = 0, !- Program Line 20 + endif, !- Program Line 21 + else, !- Program Line 22 + set meets_base_reqs = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + set meets_base_reqs = 0, !- Program Line 26 + endif, !- Program Line 27 + else, !- Program Line 28 + set meets_base_reqs = 0, !- Program Line 29 + endif, !- Program Line 30 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 31 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT>VST, !- Program Line 32 + set Ventilates_HVACmode2_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 1, !- Program Line 33 + else, !- Program Line 34 + set Ventilates_HVACmode2_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + set Ventilates_HVACmode2_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 38 + endif, !- Program Line 39 + if VentCtrl == 0, !- Program Line 40 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT < Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT, !- Program Line 41 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT>MinOutTemp, !- Program Line 42 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT > VST, !- Program Line 43 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 44 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 1, !- Program Line 45 + else, !- Program Line 46 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 47 + endif, !- Program Line 48 + else, !- Program Line 49 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 50 + endif, !- Program Line 51 + else, !- Program Line 52 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 53 + endif, !- Program Line 54 + else, !- Program Line 55 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 56 + endif, !- Program Line 57 + elseif VentCtrl == 1, !- Program Line 58 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutTMinOutTemp, !- Program Line 60 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT > ACSTnoTol, !- Program Line 61 + if Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 62 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 1, !- Program Line 63 + else, !- Program Line 64 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 65 + endif, !- Program Line 66 + else, !- Program Line 67 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 68 + endif, !- Program Line 69 + else, !- Program Line 70 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 71 + endif, !- Program Line 72 + else, !- Program Line 73 + set Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 74 + endif, !- Program Line 75 + endif, !- Program Line 76 + if HVACmode == 0, !- Program Line 77 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 78 + elseif HVACmode == 1, !- Program Line 79 + if Ventilates_HVACmode1_Block1_Zone2_Wall_3_0_0_0_0_0_Win == 1, !- Program Line 80 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact = Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF, !- Program Line 81 + else, !- Program Line 82 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 83 + endif, !- Program Line 84 + elseif HVACmode == 2, !- Program Line 85 + if Ventilates_HVACmode2_Block1_Zone2_Wall_3_0_0_0_0_0_Win == 1, !- Program Line 86 + if (VentCtrl==0) || (VentCtrl==1), !- Program Line 87 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact = 1, !- Program Line 88 + elseif (VentCtrl==2) || (VentCtrl==3), !- Program Line 89 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact = Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF, !- Program Line 90 + endif, !- Program Line 91 + else, !- Program Line 92 + set Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 93 + endif, !- Program Line 94 + endif; !- Program Line 95 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win, !- Name + set slope = (1 - MultiplierVOF) / (MinTempDiffVOF - MaxTempDiffVOF), !- Program Line 1 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT > 0, !- Program Line 2 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT, !- Program Line 3 + else, !- Program Line 4 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT + Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT, !- Program Line 5 + endif, !- Program Line 6 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT > Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT, !- Program Line 7 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF = 0, !- Program Line 8 + else, !- Program Line 9 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_TempDiffVOF > MaxTempDiffVOF, !- Program Line 10 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF = MultiplierVOF, !- Program Line 11 + elseif Block1_Zone2_Wall_4_0_0_0_0_0_Win_TempDiffVOF < MinTempDiffVOF, !- Program Line 12 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF = 1.0, !- Program Line 13 + else, !- Program Line 14 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF = slope*Block1_Zone2_Wall_4_0_0_0_0_0_Win_TempDiffVOF - slope*MinTempDiffVOF + 1, !- Program Line 15 + endif, !- Program Line 16 + endif; !- Program Line 17 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win, !- Name + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil==0, !- Program Line 1 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpTMinOutTemp, !- Program Line 12 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT < Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT, !- Program Line 13 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count > 0, !- Program Line 14 + set meets_base_reqs = 1, !- Program Line 15 + else, !- Program Line 16 + set meets_base_reqs = 0, !- Program Line 17 + endif, !- Program Line 18 + else, !- Program Line 19 + set meets_base_reqs = 0, !- Program Line 20 + endif, !- Program Line 21 + else, !- Program Line 22 + set meets_base_reqs = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + set meets_base_reqs = 0, !- Program Line 26 + endif, !- Program Line 27 + else, !- Program Line 28 + set meets_base_reqs = 0, !- Program Line 29 + endif, !- Program Line 30 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 31 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT>VST, !- Program Line 32 + set Ventilates_HVACmode2_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 1, !- Program Line 33 + else, !- Program Line 34 + set Ventilates_HVACmode2_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + set Ventilates_HVACmode2_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 38 + endif, !- Program Line 39 + if VentCtrl == 0, !- Program Line 40 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT < Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT, !- Program Line 41 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT>MinOutTemp, !- Program Line 42 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT > VST, !- Program Line 43 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 44 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 1, !- Program Line 45 + else, !- Program Line 46 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 47 + endif, !- Program Line 48 + else, !- Program Line 49 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 50 + endif, !- Program Line 51 + else, !- Program Line 52 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 53 + endif, !- Program Line 54 + else, !- Program Line 55 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 56 + endif, !- Program Line 57 + elseif VentCtrl == 1, !- Program Line 58 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutTMinOutTemp, !- Program Line 60 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT > ACSTnoTol, !- Program Line 61 + if Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 62 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 1, !- Program Line 63 + else, !- Program Line 64 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 65 + endif, !- Program Line 66 + else, !- Program Line 67 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 68 + endif, !- Program Line 69 + else, !- Program Line 70 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 71 + endif, !- Program Line 72 + else, !- Program Line 73 + set Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win = 0, !- Program Line 74 + endif, !- Program Line 75 + endif, !- Program Line 76 + if HVACmode == 0, !- Program Line 77 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 78 + elseif HVACmode == 1, !- Program Line 79 + if Ventilates_HVACmode1_Block1_Zone2_Wall_4_0_0_0_0_0_Win == 1, !- Program Line 80 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact = Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF, !- Program Line 81 + else, !- Program Line 82 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 83 + endif, !- Program Line 84 + elseif HVACmode == 2, !- Program Line 85 + if Ventilates_HVACmode2_Block1_Zone2_Wall_4_0_0_0_0_0_Win == 1, !- Program Line 86 + if (VentCtrl==0) || (VentCtrl==1), !- Program Line 87 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact = 1, !- Program Line 88 + elseif (VentCtrl==2) || (VentCtrl==3), !- Program Line 89 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact = Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF, !- Program Line 90 + endif, !- Program Line 91 + else, !- Program Line 92 + set Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 93 + endif, !- Program Line 94 + endif; !- Program Line 95 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win, !- Name + set slope = (1 - MultiplierVOF) / (MinTempDiffVOF - MaxTempDiffVOF), !- Program Line 1 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT > 0, !- Program Line 2 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT, !- Program Line 3 + else, !- Program Line 4 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT + Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT, !- Program Line 5 + endif, !- Program Line 6 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT > Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT, !- Program Line 7 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF = 0, !- Program Line 8 + else, !- Program Line 9 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_TempDiffVOF > MaxTempDiffVOF, !- Program Line 10 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF = MultiplierVOF, !- Program Line 11 + elseif Block1_Zone2_Wall_5_0_0_0_0_0_Win_TempDiffVOF < MinTempDiffVOF, !- Program Line 12 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF = 1.0, !- Program Line 13 + else, !- Program Line 14 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF = slope*Block1_Zone2_Wall_5_0_0_0_0_0_Win_TempDiffVOF - slope*MinTempDiffVOF + 1, !- Program Line 15 + endif, !- Program Line 16 + endif; !- Program Line 17 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win, !- Name + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil==0, !- Program Line 1 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpTMinOutTemp, !- Program Line 12 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT < Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT, !- Program Line 13 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count > 0, !- Program Line 14 + set meets_base_reqs = 1, !- Program Line 15 + else, !- Program Line 16 + set meets_base_reqs = 0, !- Program Line 17 + endif, !- Program Line 18 + else, !- Program Line 19 + set meets_base_reqs = 0, !- Program Line 20 + endif, !- Program Line 21 + else, !- Program Line 22 + set meets_base_reqs = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + set meets_base_reqs = 0, !- Program Line 26 + endif, !- Program Line 27 + else, !- Program Line 28 + set meets_base_reqs = 0, !- Program Line 29 + endif, !- Program Line 30 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 31 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT>VST, !- Program Line 32 + set Ventilates_HVACmode2_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 1, !- Program Line 33 + else, !- Program Line 34 + set Ventilates_HVACmode2_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + set Ventilates_HVACmode2_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 38 + endif, !- Program Line 39 + if VentCtrl == 0, !- Program Line 40 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT < Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT, !- Program Line 41 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT>MinOutTemp, !- Program Line 42 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT > VST, !- Program Line 43 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 44 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 1, !- Program Line 45 + else, !- Program Line 46 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 47 + endif, !- Program Line 48 + else, !- Program Line 49 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 50 + endif, !- Program Line 51 + else, !- Program Line 52 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 53 + endif, !- Program Line 54 + else, !- Program Line 55 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 56 + endif, !- Program Line 57 + elseif VentCtrl == 1, !- Program Line 58 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutTMinOutTemp, !- Program Line 60 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT > ACSTnoTol, !- Program Line 61 + if Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 62 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 1, !- Program Line 63 + else, !- Program Line 64 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 65 + endif, !- Program Line 66 + else, !- Program Line 67 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 68 + endif, !- Program Line 69 + else, !- Program Line 70 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 71 + endif, !- Program Line 72 + else, !- Program Line 73 + set Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 74 + endif, !- Program Line 75 + endif, !- Program Line 76 + if HVACmode == 0, !- Program Line 77 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 78 + elseif HVACmode == 1, !- Program Line 79 + if Ventilates_HVACmode1_Block1_Zone2_Wall_5_0_0_0_0_0_Win == 1, !- Program Line 80 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact = Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF, !- Program Line 81 + else, !- Program Line 82 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 83 + endif, !- Program Line 84 + elseif HVACmode == 2, !- Program Line 85 + if Ventilates_HVACmode2_Block1_Zone2_Wall_5_0_0_0_0_0_Win == 1, !- Program Line 86 + if (VentCtrl==0) || (VentCtrl==1), !- Program Line 87 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact = 1, !- Program Line 88 + elseif (VentCtrl==2) || (VentCtrl==3), !- Program Line 89 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact = Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF, !- Program Line 90 + endif, !- Program Line 91 + else, !- Program Line 92 + set Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 93 + endif, !- Program Line 94 + endif; !- Program Line 95 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win, !- Name + set slope = (1 - MultiplierVOF) / (MinTempDiffVOF - MaxTempDiffVOF), !- Program Line 1 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT > 0, !- Program Line 2 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT, !- Program Line 3 + else, !- Program Line 4 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT + Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT, !- Program Line 5 + endif, !- Program Line 6 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT > Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT, !- Program Line 7 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF = 0, !- Program Line 8 + else, !- Program Line 9 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_TempDiffVOF > MaxTempDiffVOF, !- Program Line 10 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF = MultiplierVOF, !- Program Line 11 + elseif Block1_Zone1_Wall_2_0_0_0_0_0_Win_TempDiffVOF < MinTempDiffVOF, !- Program Line 12 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF = 1.0, !- Program Line 13 + else, !- Program Line 14 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF = slope*Block1_Zone1_Wall_2_0_0_0_0_0_Win_TempDiffVOF - slope*MinTempDiffVOF + 1, !- Program Line 15 + endif, !- Program Line 16 + endif; !- Program Line 17 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win, !- Name + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil==0, !- Program Line 1 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpTMinOutTemp, !- Program Line 12 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT < Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT, !- Program Line 13 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count > 0, !- Program Line 14 + set meets_base_reqs = 1, !- Program Line 15 + else, !- Program Line 16 + set meets_base_reqs = 0, !- Program Line 17 + endif, !- Program Line 18 + else, !- Program Line 19 + set meets_base_reqs = 0, !- Program Line 20 + endif, !- Program Line 21 + else, !- Program Line 22 + set meets_base_reqs = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + set meets_base_reqs = 0, !- Program Line 26 + endif, !- Program Line 27 + else, !- Program Line 28 + set meets_base_reqs = 0, !- Program Line 29 + endif, !- Program Line 30 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 31 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT>VST, !- Program Line 32 + set Ventilates_HVACmode2_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 1, !- Program Line 33 + else, !- Program Line 34 + set Ventilates_HVACmode2_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + set Ventilates_HVACmode2_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 38 + endif, !- Program Line 39 + if VentCtrl == 0, !- Program Line 40 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT < Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT, !- Program Line 41 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT>MinOutTemp, !- Program Line 42 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT > VST, !- Program Line 43 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 44 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 1, !- Program Line 45 + else, !- Program Line 46 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 47 + endif, !- Program Line 48 + else, !- Program Line 49 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 50 + endif, !- Program Line 51 + else, !- Program Line 52 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 53 + endif, !- Program Line 54 + else, !- Program Line 55 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 56 + endif, !- Program Line 57 + elseif VentCtrl == 1, !- Program Line 58 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutTMinOutTemp, !- Program Line 60 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT > ACSTnoTol, !- Program Line 61 + if Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 62 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 1, !- Program Line 63 + else, !- Program Line 64 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 65 + endif, !- Program Line 66 + else, !- Program Line 67 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 68 + endif, !- Program Line 69 + else, !- Program Line 70 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 71 + endif, !- Program Line 72 + else, !- Program Line 73 + set Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win = 0, !- Program Line 74 + endif, !- Program Line 75 + endif, !- Program Line 76 + if HVACmode == 0, !- Program Line 77 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 78 + elseif HVACmode == 1, !- Program Line 79 + if Ventilates_HVACmode1_Block1_Zone1_Wall_2_0_0_0_0_0_Win == 1, !- Program Line 80 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact = Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF, !- Program Line 81 + else, !- Program Line 82 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 83 + endif, !- Program Line 84 + elseif HVACmode == 2, !- Program Line 85 + if Ventilates_HVACmode2_Block1_Zone1_Wall_2_0_0_0_0_0_Win == 1, !- Program Line 86 + if (VentCtrl==0) || (VentCtrl==1), !- Program Line 87 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact = 1, !- Program Line 88 + elseif (VentCtrl==2) || (VentCtrl==3), !- Program Line 89 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact = Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF, !- Program Line 90 + endif, !- Program Line 91 + else, !- Program Line 92 + set Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 93 + endif, !- Program Line 94 + endif; !- Program Line 95 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win, !- Name + set slope = (1 - MultiplierVOF) / (MinTempDiffVOF - MaxTempDiffVOF), !- Program Line 1 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT > 0, !- Program Line 2 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT, !- Program Line 3 + else, !- Program Line 4 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT + Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT, !- Program Line 5 + endif, !- Program Line 6 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT > Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT, !- Program Line 7 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF = 0, !- Program Line 8 + else, !- Program Line 9 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_TempDiffVOF > MaxTempDiffVOF, !- Program Line 10 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF = MultiplierVOF, !- Program Line 11 + elseif Block1_Zone1_Wall_3_0_0_0_0_0_Win_TempDiffVOF < MinTempDiffVOF, !- Program Line 12 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF = 1.0, !- Program Line 13 + else, !- Program Line 14 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF = slope*Block1_Zone1_Wall_3_0_0_0_0_0_Win_TempDiffVOF - slope*MinTempDiffVOF + 1, !- Program Line 15 + endif, !- Program Line 16 + endif; !- Program Line 17 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win, !- Name + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil==0, !- Program Line 1 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpTMinOutTemp, !- Program Line 12 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT < Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT, !- Program Line 13 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count > 0, !- Program Line 14 + set meets_base_reqs = 1, !- Program Line 15 + else, !- Program Line 16 + set meets_base_reqs = 0, !- Program Line 17 + endif, !- Program Line 18 + else, !- Program Line 19 + set meets_base_reqs = 0, !- Program Line 20 + endif, !- Program Line 21 + else, !- Program Line 22 + set meets_base_reqs = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + set meets_base_reqs = 0, !- Program Line 26 + endif, !- Program Line 27 + else, !- Program Line 28 + set meets_base_reqs = 0, !- Program Line 29 + endif, !- Program Line 30 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 31 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT>VST, !- Program Line 32 + set Ventilates_HVACmode2_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 1, !- Program Line 33 + else, !- Program Line 34 + set Ventilates_HVACmode2_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + set Ventilates_HVACmode2_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 38 + endif, !- Program Line 39 + if VentCtrl == 0, !- Program Line 40 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT < Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT, !- Program Line 41 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT>MinOutTemp, !- Program Line 42 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT > VST, !- Program Line 43 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 44 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 1, !- Program Line 45 + else, !- Program Line 46 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 47 + endif, !- Program Line 48 + else, !- Program Line 49 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 50 + endif, !- Program Line 51 + else, !- Program Line 52 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 53 + endif, !- Program Line 54 + else, !- Program Line 55 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 56 + endif, !- Program Line 57 + elseif VentCtrl == 1, !- Program Line 58 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutTMinOutTemp, !- Program Line 60 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT > ACSTnoTol, !- Program Line 61 + if Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 62 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 1, !- Program Line 63 + else, !- Program Line 64 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 65 + endif, !- Program Line 66 + else, !- Program Line 67 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 68 + endif, !- Program Line 69 + else, !- Program Line 70 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 71 + endif, !- Program Line 72 + else, !- Program Line 73 + set Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win = 0, !- Program Line 74 + endif, !- Program Line 75 + endif, !- Program Line 76 + if HVACmode == 0, !- Program Line 77 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 78 + elseif HVACmode == 1, !- Program Line 79 + if Ventilates_HVACmode1_Block1_Zone1_Wall_3_0_0_0_0_0_Win == 1, !- Program Line 80 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact = Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF, !- Program Line 81 + else, !- Program Line 82 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 83 + endif, !- Program Line 84 + elseif HVACmode == 2, !- Program Line 85 + if Ventilates_HVACmode2_Block1_Zone1_Wall_3_0_0_0_0_0_Win == 1, !- Program Line 86 + if (VentCtrl==0) || (VentCtrl==1), !- Program Line 87 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact = 1, !- Program Line 88 + elseif (VentCtrl==2) || (VentCtrl==3), !- Program Line 89 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact = Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF, !- Program Line 90 + endif, !- Program Line 91 + else, !- Program Line 92 + set Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 93 + endif, !- Program Line 94 + endif; !- Program Line 95 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win, !- Name + set slope = (1 - MultiplierVOF) / (MinTempDiffVOF - MaxTempDiffVOF), !- Program Line 1 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT > 0, !- Program Line 2 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT, !- Program Line 3 + else, !- Program Line 4 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_TempDiffVOF = Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT + Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT, !- Program Line 5 + endif, !- Program Line 6 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT > Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT, !- Program Line 7 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF = 0, !- Program Line 8 + else, !- Program Line 9 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_TempDiffVOF > MaxTempDiffVOF, !- Program Line 10 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF = MultiplierVOF, !- Program Line 11 + elseif Block1_Zone1_Wall_5_0_0_0_0_0_Win_TempDiffVOF < MinTempDiffVOF, !- Program Line 12 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF = 1.0, !- Program Line 13 + else, !- Program Line 14 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF = slope*Block1_Zone1_Wall_5_0_0_0_0_0_Win_TempDiffVOF - slope*MinTempDiffVOF + 1, !- Program Line 15 + endif, !- Program Line 16 + endif; !- Program Line 17 + +ENERGYMANAGEMENTSYSTEM:PROGRAM, + SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win, !- Name + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil==0, !- Program Line 1 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil==0, !- Program Line 2 + set NoH_NoC_reqs = 1, !- Program Line 3 + else, !- Program Line 4 + set NoH_NoC_reqs = 0, !- Program Line 5 + endif, !- Program Line 6 + else, !- Program Line 7 + set NoH_NoC_reqs = 0, !- Program Line 8 + endif, !- Program Line 9 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpTMinOutTemp, !- Program Line 12 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT < Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT, !- Program Line 13 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count > 0, !- Program Line 14 + set meets_base_reqs = 1, !- Program Line 15 + else, !- Program Line 16 + set meets_base_reqs = 0, !- Program Line 17 + endif, !- Program Line 18 + else, !- Program Line 19 + set meets_base_reqs = 0, !- Program Line 20 + endif, !- Program Line 21 + else, !- Program Line 22 + set meets_base_reqs = 0, !- Program Line 23 + endif, !- Program Line 24 + else, !- Program Line 25 + set meets_base_reqs = 0, !- Program Line 26 + endif, !- Program Line 27 + else, !- Program Line 28 + set meets_base_reqs = 0, !- Program Line 29 + endif, !- Program Line 30 + if (NoH_NoC_reqs == 1) && (meets_base_reqs == 1), !- Program Line 31 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT>VST, !- Program Line 32 + set Ventilates_HVACmode2_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 1, !- Program Line 33 + else, !- Program Line 34 + set Ventilates_HVACmode2_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 35 + endif, !- Program Line 36 + else, !- Program Line 37 + set Ventilates_HVACmode2_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 38 + endif, !- Program Line 39 + if VentCtrl == 0, !- Program Line 40 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT < Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT, !- Program Line 41 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT>MinOutTemp, !- Program Line 42 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT > VST, !- Program Line 43 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 44 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 1, !- Program Line 45 + else, !- Program Line 46 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 47 + endif, !- Program Line 48 + else, !- Program Line 49 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 50 + endif, !- Program Line 51 + else, !- Program Line 52 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 53 + endif, !- Program Line 54 + else, !- Program Line 55 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 56 + endif, !- Program Line 57 + elseif VentCtrl == 1, !- Program Line 58 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutTMinOutTemp, !- Program Line 60 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT > ACSTnoTol, !- Program Line 61 + if Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed <= MaxWindSpeed, !- Program Line 62 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 1, !- Program Line 63 + else, !- Program Line 64 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 65 + endif, !- Program Line 66 + else, !- Program Line 67 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 68 + endif, !- Program Line 69 + else, !- Program Line 70 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 71 + endif, !- Program Line 72 + else, !- Program Line 73 + set Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win = 0, !- Program Line 74 + endif, !- Program Line 75 + endif, !- Program Line 76 + if HVACmode == 0, !- Program Line 77 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 78 + elseif HVACmode == 1, !- Program Line 79 + if Ventilates_HVACmode1_Block1_Zone1_Wall_5_0_0_0_0_0_Win == 1, !- Program Line 80 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact = Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF, !- Program Line 81 + else, !- Program Line 82 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 83 + endif, !- Program Line 84 + elseif HVACmode == 2, !- Program Line 85 + if Ventilates_HVACmode2_Block1_Zone1_Wall_5_0_0_0_0_0_Win == 1, !- Program Line 86 + if (VentCtrl==0) || (VentCtrl==1), !- Program Line 87 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact = 1, !- Program Line 88 + elseif (VentCtrl==2) || (VentCtrl==3), !- Program Line 89 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact = Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF, !- Program Line 90 + endif, !- Program Line 91 + else, !- Program Line 92 + set Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact = 0, !- Program Line 93 + endif, !- Program Line 94 + endif; !- Program Line 95 + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + ACST, !- Erl Variable 1 Name + AHST, !- Erl Variable 2 Name + ACSTnoTol, !- Erl Variable 3 Name + AHSTnoTol, !- Erl Variable 4 Name + ComfStand, !- Erl Variable 5 Name + ACSTaul, !- Erl Variable 6 Name + ACSTall, !- Erl Variable 7 Name + AHSTaul, !- Erl Variable 8 Name + AHSTall, !- Erl Variable 9 Name + CAT, !- Erl Variable 10 Name + ACSToffset, !- Erl Variable 11 Name + AHSToffset, !- Erl Variable 12 Name + ComfMod, !- Erl Variable 13 Name + ComfTemp, !- Erl Variable 14 Name + ACSTtol, !- Erl Variable 15 Name + AHSTtol, !- Erl Variable 16 Name + SetpointAcc, !- Erl Variable 17 Name + roundedACST, !- Erl Variable 18 Name + roundedAHST, !- Erl Variable 19 Name + CoolSeasonStart, !- Erl Variable 20 Name + CoolSeasonEnd, !- Erl Variable 21 Name + m, !- Erl Variable 22 Name + n; !- Erl Variable 23 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + ComfHours_BLOCK1_ZONE2, !- Erl Variable 1 Name + DiscomfAppHotHours_BLOCK1_ZONE2, !- Erl Variable 2 Name + DiscomfAppColdHours_BLOCK1_ZONE2, !- Erl Variable 3 Name + DiscomfNonAppHotHours_BLOCK1_ZONE2, !- Erl Variable 4 Name + DiscomfNonAppColdHours_BLOCK1_ZONE2, !- Erl Variable 5 Name + ComfHoursNoApp_BLOCK1_ZONE2, !- Erl Variable 6 Name + ZoneFloorArea_BLOCK1_ZONE2, !- Erl Variable 7 Name + ZoneAirVolume_BLOCK1_ZONE2, !- Erl Variable 8 Name + OccHours_BLOCK1_ZONE2, !- Erl Variable 9 Name + OccComfHoursNoApp_BLOCK1_ZONE2, !- Erl Variable 10 Name + OccDiscomfHoursNoApp_BLOCK1_ZONE2; !- Erl Variable 11 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + ComfHours_BLOCK1_ZONE1, !- Erl Variable 1 Name + DiscomfAppHotHours_BLOCK1_ZONE1, !- Erl Variable 2 Name + DiscomfAppColdHours_BLOCK1_ZONE1, !- Erl Variable 3 Name + DiscomfNonAppHotHours_BLOCK1_ZONE1, !- Erl Variable 4 Name + DiscomfNonAppColdHours_BLOCK1_ZONE1, !- Erl Variable 5 Name + ComfHoursNoApp_BLOCK1_ZONE1, !- Erl Variable 6 Name + ZoneFloorArea_BLOCK1_ZONE1, !- Erl Variable 7 Name + ZoneAirVolume_BLOCK1_ZONE1, !- Erl Variable 8 Name + OccHours_BLOCK1_ZONE1, !- Erl Variable 9 Name + OccComfHoursNoApp_BLOCK1_ZONE1, !- Erl Variable 10 Name + OccDiscomfHoursNoApp_BLOCK1_ZONE1; !- Erl Variable 11 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + VST, !- Erl Variable 1 Name + VSToffset, !- Erl Variable 2 Name + MaxWindSpeed, !- Erl Variable 3 Name + VentCtrl, !- Erl Variable 4 Name + HVACmode, !- Erl Variable 5 Name + MinOutTemp, !- Erl Variable 6 Name + MinOToffset, !- Erl Variable 7 Name + MaxTempDiffVOF, !- Erl Variable 8 Name + MinTempDiffVOF, !- Erl Variable 9 Name + MultiplierVOF; !- Erl Variable 10 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + VentHours_BLOCK1_ZONE2; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + VentHours_BLOCK1_ZONE1; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + Block1_Zone2_Wall_3_0_0_0_0_0_Win_MyVOF; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + Block1_Zone2_Wall_4_0_0_0_0_0_Win_MyVOF; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + Block1_Zone2_Wall_5_0_0_0_0_0_Win_MyVOF; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + Block1_Zone1_Wall_2_0_0_0_0_0_Win_MyVOF; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + Block1_Zone1_Wall_3_0_0_0_0_0_Win_MyVOF; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:GLOBALVARIABLE, + Block1_Zone1_Wall_5_0_0_0_0_0_Win_MyVOF; !- Erl Variable 1 Name + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Comfort Temperature, !- Name + ComfTemp, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Adaptive Cooling Setpoint Temperature, !- Name + roundedACST, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Adaptive Heating Setpoint Temperature, !- Name + roundedAHST, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Adaptive Cooling Setpoint Temperature_No Tolerance, !- Name + ACSTnoTol, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Adaptive Heating Setpoint Temperature_No Tolerance, !- Name + AHSTnoTol, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_ComfStand, !- Name + ComfStand, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + NA; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_ACSTaul, !- Name + ACSTaul, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_ACSTall, !- Name + ACSTall, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_AHSTaul, !- Name + AHSTaul, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_AHSTall, !- Name + AHSTall, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_CAT, !- Name + CAT, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_ACSToffset, !- Name + ACSToffset, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_AHSToffset, !- Name + AHSToffset, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_ComfMod, !- Name + ComfMod, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + NA; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_ACSTtol, !- Name + ACSTtol, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_SetpointAcc, !- Name + SetpointAcc, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + NA; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_CustAST_m, !- Name + m, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + NA; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_CustAST_n, !- Name + n, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + NA; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + z_test_AHSTtol, !- Name + AHSTtol, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Ventilation Setpoint Temperature, !- Name + VST, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Minimum Outdoor Temperature for ventilation, !- Name + MinOutTemp, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Minimum Outdoor Temperature Difference for ventilation, !- Name + MinTempDiffVOF, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Maximum Outdoor Temperature Difference for ventilation, !- Name + MaxTempDiffVOF, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + C; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Multiplier for Ventilation Opening Factor, !- Name + MultiplierVOF, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + ; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Comfortable Hours_No Applicability_BLOCK1_ZONE2, !- Name + ComfHoursNoApp_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Comfortable Hours_No Applicability_BLOCK1_ZONE1, !- Name + ComfHoursNoApp_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Comfortable Hours_Applicability_BLOCK1_ZONE2, !- Name + ComfHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Comfortable Hours_Applicability_BLOCK1_ZONE1, !- Name + ComfHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2, !- Name + OccComfHoursNoApp_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1, !- Name + OccComfHoursNoApp_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2, !- Name + OccDiscomfHoursNoApp_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1, !- Name + OccDiscomfHoursNoApp_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Occupied Hours_BLOCK1_ZONE2, !- Name + OccHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Occupied Hours_BLOCK1_ZONE1, !- Name + OccHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Applicable Hot Hours_BLOCK1_ZONE2, !- Name + DiscomfAppHotHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Applicable Hot Hours_BLOCK1_ZONE1, !- Name + DiscomfAppHotHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Applicable Cold Hours_BLOCK1_ZONE2, !- Name + DiscomfAppColdHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Applicable Cold Hours_BLOCK1_ZONE1, !- Name + DiscomfAppColdHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2, !- Name + DiscomfNonAppHotHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1, !- Name + DiscomfNonAppHotHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2, !- Name + DiscomfNonAppColdHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1, !- Name + DiscomfNonAppColdHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Zone Floor Area_BLOCK1_ZONE2, !- Name + ZoneFloorArea_BLOCK1_ZONE2, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + m2; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Zone Floor Area_BLOCK1_ZONE1, !- Name + ZoneFloorArea_BLOCK1_ZONE1, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + m2; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Zone Air Volume_BLOCK1_ZONE2, !- Name + ZoneAirVolume_BLOCK1_ZONE2, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + m3; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Zone Air Volume_BLOCK1_ZONE1, !- Name + ZoneAirVolume_BLOCK1_ZONE1, !- EMS Variable Name + Averaged, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + m3; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + People Occupant Count_BLOCK1_ZONE2, !- Name + Occ_count_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + ; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + People Occupant Count_BLOCK1_ZONE1, !- Name + Occ_count_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + ; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Ventilation Hours_BLOCK1_ZONE2, !- Name + VentHours_BLOCK1_ZONE2, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:OUTPUTVARIABLE, + Ventilation Hours_BLOCK1_ZONE1, !- Name + VentHours_BLOCK1_ZONE1, !- EMS Variable Name + Summed, !- Type of Data in Variable + ZoneTimestep, !- Update Frequency + , !- EMS Program or Subroutine Name + H; !- Units + +ENERGYMANAGEMENTSYSTEM:INTERNALVARIABLE, + ZFA_BLOCK1_ZONE2, !- Name + BLOCK1:ZONE2, !- Internal Data Index Key Name + Zone Floor Area; !- Internal Data Type + +ENERGYMANAGEMENTSYSTEM:INTERNALVARIABLE, + ZAV_BLOCK1_ZONE2, !- Name + BLOCK1:ZONE2, !- Internal Data Index Key Name + Zone Air Volume; !- Internal Data Type + +ENERGYMANAGEMENTSYSTEM:INTERNALVARIABLE, + ZFA_BLOCK1_ZONE1, !- Name + BLOCK1:ZONE1, !- Internal Data Index Key Name + Zone Floor Area; !- Internal Data Type + +ENERGYMANAGEMENTSYSTEM:INTERNALVARIABLE, + ZAV_BLOCK1_ZONE1, !- Name + BLOCK1:ZONE1, !- Internal Data Index Key Name + Zone Air Volume; !- Internal Data Type + +WaterUse:Equipment, + DHW Block1:Zone2, !- Name + DHW Block1:Zone2, !- EndUse Subcategory + 2.234679e-07, !- Peak Flow Rate + Office_OpenOff_Occ, !- Flow Rate Fraction Schedule Name + DHW Supply Temperature Block1:Zone2, !- Target Temperature Schedule Name + DHW Supply Temperature Block1:Zone2, !- Hot Water Supply Temperature Schedule Name + DHW Mains Temperature Block1:Zone2, !- Cold Water Supply Temperature Schedule Name + Block1:Zone2, !- Zone Name + , !- Sensible Fraction Schedule Name + ; !- Latent Fraction Schedule Name + +WaterUse:Equipment, + DHW Block1:Zone1, !- Name + DHW Block1:Zone1, !- EndUse Subcategory + 1.857735e-07, !- Peak Flow Rate + Office_OpenOff_Occ, !- Flow Rate Fraction Schedule Name + DHW Supply Temperature Block1:Zone1, !- Target Temperature Schedule Name + DHW Supply Temperature Block1:Zone1, !- Hot Water Supply Temperature Schedule Name + DHW Mains Temperature Block1:Zone1, !- Cold Water Supply Temperature Schedule Name + Block1:Zone1, !- Zone Name + , !- Sensible Fraction Schedule Name + ; !- Latent Fraction Schedule Name + +CURVE:LINEAR, + CoolingCombRatio, !- Name + 0.618055, !- Coefficient1 Constant + 0.381945, !- Coefficient2 x + 1.0, !- Minimum Value of x + 1.5, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:LINEAR, + HeatingCombRatio, !- Name + 0.96034, !- Coefficient1 Constant + 0.03966, !- Coefficient2 x + 1.0, !- Minimum Value of x + 1.5, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:QUADRATIC, + VRFACCoolCapFFF, !- Name + 0.8, !- Coefficient1 Constant + 0.2, !- Coefficient2 x + 0.0, !- Coefficient3 x2 + 0.5, !- Minimum Value of x + 1.5, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:QUADRATIC, + CoolingEIRHiPLR, !- Name + 1.0, !- Coefficient1 Constant + 0.0, !- Coefficient2 x + 0.0, !- Coefficient3 x2 + 1.0, !- Minimum Value of x + 1.5, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:QUADRATIC, + VRFCPLFFPLR, !- Name + 0.85, !- Coefficient1 Constant + 0.15, !- Coefficient2 x + 0.0, !- Coefficient3 x2 + 0.0, !- Minimum Value of x + 1.0, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:QUADRATIC, + HeatingEIRHiPLR, !- Name + 2.4294355, !- Coefficient1 Constant + -2.235887, !- Coefficient2 x + 0.8064516, !- Coefficient3 x2 + 1.0, !- Minimum Value of x + 1.5, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:CUBIC, + DefaultFanEffRatioCurve, !- Name + 0.33856828, !- Coefficient1 Constant + 1.72644131, !- Coefficient2 x + -1.49280132, !- Coefficient3 x2 + 0.42776208, !- Coefficient4 x3 + 0.5, !- Minimum Value of x + 1.5, !- Maximum Value of x + 0.3, !- Minimum Curve Output + 1.0, !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:CUBIC, + VRFTUCoolCapFT, !- Name + 0.504547273506488, !- Coefficient1 Constant + 0.0288891279198444, !- Coefficient2 x + -0.000010819418650677, !- Coefficient3 x2 + 0.0000101359395177008, !- Coefficient4 x3 + 0.0, !- Minimum Value of x + 50.0, !- Maximum Value of x + 0.5, !- Minimum Curve Output + 1.5, !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Dimensionless; !- Output Unit Type + +CURVE:CUBIC, + VRFTUHeatCapFT, !- Name + -0.390708928227928, !- Coefficient1 Constant + 0.261815023760162, !- Coefficient2 x + -0.0130431603151873, !- Coefficient3 x2 + 0.000178131745997821, !- Coefficient4 x3 + 0.0, !- Minimum Value of x + 50.0, !- Maximum Value of x + 0.5, !- Minimum Curve Output + 1.5, !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Dimensionless; !- Output Unit Type + +CURVE:CUBIC, + VRFCoolCapFTBoundary, !- Name + 25.73473775, !- Coefficient1 Constant + -0.03150043, !- Coefficient2 x + -0.01416595, !- Coefficient3 x2 + 0, !- Coefficient4 x3 + 11, !- Minimum Value of x + 30, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:CUBIC, + VRFCoolEIRFTBoundary, !- Name + 25.73473775, !- Coefficient1 Constant + -0.03150043, !- Coefficient2 x + -0.01416595, !- Coefficient3 x2 + 0, !- Coefficient4 x3 + 15, !- Minimum Value of x + 24, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:CUBIC, + CoolingEIRLowPLR, !- Name + 0.4628123, !- Coefficient1 Constant + -1.0402406, !- Coefficient2 x + 2.17490997, !- Coefficient3 x2 + -0.5974817, !- Coefficient4 x3 + 0, !- Minimum Value of x + 1, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Capacity; !- Output Unit Type + +CURVE:CUBIC, + VRFHeatCapFTBoundary, !- Name + -7.6000882, !- Coefficient1 Constant + 3.05090016, !- Coefficient2 x + -0.1162844, !- Coefficient3 x2 + 0.0, !- Coefficient4 x3 + 15, !- Minimum Value of x + 27, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:CUBIC, + VRFHeatEIRFTBoundary, !- Name + -7.6000882, !- Coefficient1 Constant + 3.05090016, !- Coefficient2 x + -0.1162844, !- Coefficient3 x2 + 0.0, !- Coefficient4 x3 + 15, !- Minimum Value of x + 27, !- Maximum Value of x + -20, !- Minimum Curve Output + 15, !- Maximum Curve Output + Temperature, !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:CUBIC, + HeatingEIRLowPLR, !- Name + 0.1400093, !- Coefficient1 Constant + 0.6415002, !- Coefficient2 x + 0.1339047, !- Coefficient3 x2 + 0.0845859, !- Coefficient4 x3 + 0, !- Minimum Value of x + 1, !- Maximum Value of x + , !- Minimum Curve Output + , !- Maximum Curve Output + Dimensionless, !- Input Unit Type for X + Dimensionless; !- Output Unit Type + +CURVE:EXPONENT, + DefaultFanPowerRatioCurve, !- Name + 0, !- Coefficient1 Constant + 1, !- Coefficient2 Constant + 3, !- Coefficient3 Constant + 0, !- Minimum Value of x + 1.5, !- Maximum Value of x + 0.01, !- Minimum Curve Output + 1.5, !- Maximum Curve Output + , !- Input Unit Type for X + ; !- Output Unit Type + +CURVE:BIQUADRATIC, + DXHtgCoilDefrostEIRFT, !- Name + 1.0, !- Coefficient1 Constant + 0.0, !- Coefficient2 x + 0.0, !- Coefficient3 x2 + 0.0, !- Coefficient4 y + 0, !- Coefficient5 y2 + 0, !- Coefficient6 xy + 0.0, !- Minimum Value of x + 50.0, !- Maximum Value of x + 0.0, !- Minimum Value of y + 50.0, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFCoolCapFT, !- Name + 0.576882692, !- Coefficient1 Constant + 0.017447952, !- Coefficient2 x + 0.000583269, !- Coefficient3 x2 + -1.76324E-06, !- Coefficient4 y + -7.474E-09, !- Coefficient5 y2 + -1.30413E-07, !- Coefficient6 xy + 15, !- Minimum Value of x + 24, !- Maximum Value of x + -5, !- Minimum Value of y + 23, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFCoolCapFTHi, !- Name + 0.6867358, !- Coefficient1 Constant + 0.0207631, !- Coefficient2 x + 0.0005447, !- Coefficient3 x2 + -0.0016218, !- Coefficient4 y + -4.259E-07, !- Coefficient5 y2 + -0.0003392, !- Coefficient6 xy + 15, !- Minimum Value of x + 24, !- Maximum Value of x + 16, !- Minimum Value of y + 43, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFCoolEIRFT, !- Name + 0.989010541, !- Coefficient1 Constant + -0.02347967, !- Coefficient2 x + 0.000199711, !- Coefficient3 x2 + 0.005968336, !- Coefficient4 y + -1.0289E-07, !- Coefficient5 y2 + -0.00015686, !- Coefficient6 xy + 15, !- Minimum Value of x + 24, !- Maximum Value of x + -5, !- Minimum Value of y + 23, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFCoolEIRFTHi, !- Name + 0.14351470, !- Coefficient1 Constant + 0.01860035, !- Coefficient2 x + -0.0003954, !- Coefficient3 x2 + 0.02485219, !- Coefficient4 y + 0.00016329, !- Coefficient5 y2 + -0.0006244, !- Coefficient6 xy + 15, !- Minimum Value of x + 24, !- Maximum Value of x + 16, !- Minimum Value of y + 43, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFHeatCapFT, !- Name + 1.014599599, !- Coefficient1 Constant + -0.002506703, !- Coefficient2 x + -0.000141599, !- Coefficient3 x2 + 0.026931595, !- Coefficient4 y + 1.83538E-06, !- Coefficient5 y2 + -0.000358147, !- Coefficient6 xy + 15, !- Minimum Value of x + 27, !- Maximum Value of x + -20, !- Minimum Value of y + 15, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFHeatCapFTHi, !- Name + 1.161134821, !- Coefficient1 Constant + 0.027478868, !- Coefficient2 x + -0.00168795, !- Coefficient3 x2 + 0.001783378, !- Coefficient4 y + 2.03208E-06, !- Coefficient5 y2 + -6.8969E-05, !- Coefficient6 xy + 15, !- Minimum Value of x + 27, !- Maximum Value of x + -10, !- Minimum Value of y + 15, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFHeatEIRFT, !- Name + 0.87465501, !- Coefficient1 Constant + -0.01319754, !- Coefficient2 x + 0.00110307, !- Coefficient3 x2 + -0.0133118, !- Coefficient4 y + 0.00089017, !- Coefficient5 y2 + -0.00012766, !- Coefficient6 xy + 15, !- Minimum Value of x + 27, !- Maximum Value of x + -20, !- Minimum Value of y + 12, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRFHeatEIRFTHi, !- Name + 2.504005146, !- Coefficient1 Constant + -0.05736767, !- Coefficient2 x + 4.07336E-05, !- Coefficient3 x2 + -0.12959669, !- Coefficient4 y + 0.00135839, !- Coefficient5 y2 + 0.00317047, !- Coefficient6 xy + 15, !- Minimum Value of x + 27, !- Maximum Value of x + -10, !- Minimum Value of y + 15, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + CoolingLengthCorrectionFactor, !- Name + 1.0693794, !- Coefficient1 Constant + -0.0014951, !- Coefficient2 x + 2.56E-06, !- Coefficient3 x2 + -0.1151104, !- Coefficient4 y + 0.0511169, !- Coefficient5 y2 + -0.0004369, !- Coefficient6 xy + 8, !- Minimum Value of x + 175, !- Maximum Value of x + 0.5, !- Minimum Value of y + 1.5, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRF Piping Correction Factor for Length in Heating Mode, !- Name + .989916, !- Coefficient1 Constant + .001961, !- Coefficient2 x + -.000036, !- Coefficient3 x2 + 0, !- Coefficient4 y + 0, !- Coefficient5 y2 + 0, !- Coefficient6 xy + 7, !- Minimum Value of x + 106.5, !- Maximum Value of x + 1, !- Minimum Value of y + 1, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Distance, !- Input Unit Type for X + Dimensionless, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRF Heat Recovery Cooling Capacity Modifier, !- Name + .9, !- Coefficient1 Constant + 0, !- Coefficient2 x + 0, !- Coefficient3 x2 + 0, !- Coefficient4 y + 0, !- Coefficient5 y2 + 0, !- Coefficient6 xy + -100, !- Minimum Value of x + 100, !- Maximum Value of x + -100, !- Minimum Value of y + 100, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRF Heat Recovery Cooling Energy Modifier, !- Name + 1.1, !- Coefficient1 Constant + 0, !- Coefficient2 x + 0, !- Coefficient3 x2 + 0, !- Coefficient4 y + 0, !- Coefficient5 y2 + 0, !- Coefficient6 xy + -100, !- Minimum Value of x + 100, !- Maximum Value of x + -100, !- Minimum Value of y + 100, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRF Heat Recovery Heating Capacity Modifier, !- Name + .9, !- Coefficient1 Constant + 0, !- Coefficient2 x + 0, !- Coefficient3 x2 + 0, !- Coefficient4 y + 0, !- Coefficient5 y2 + 0, !- Coefficient6 xy + -100, !- Minimum Value of x + 100, !- Maximum Value of x + -100, !- Minimum Value of y + 100, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +CURVE:BIQUADRATIC, + VRF Heat Recovery Heating Energy Modifier, !- Name + 1.1, !- Coefficient1 Constant + 0, !- Coefficient2 x + 0, !- Coefficient3 x2 + 0, !- Coefficient4 y + 0, !- Coefficient5 y2 + 0, !- Coefficient6 xy + -100, !- Minimum Value of x + 100, !- Maximum Value of x + -100, !- Minimum Value of y + 100, !- Maximum Value of y + , !- Minimum Curve Output + , !- Maximum Curve Output + Temperature, !- Input Unit Type for X + Temperature, !- Input Unit Type for Y + Dimensionless; !- Output Unit Type + +OUTPUT:VARIABLEDICTIONARY, + IDF, !- Key Field + Unsorted; !- Sort Option + +Output:Surfaces:List, + Details; !- Report Type + +Output:Surfaces:Drawing, + DXF, !- Report Type + Triangulate3DFace; !- Report Specifications 1 + +Output:Constructions, + Constructions; !- Details Type 1 + +Output:Table:SummaryReports, + AnnualBuildingUtilityPerformanceSummary, !- Report 1 Name + DemandEndUseComponentsSummary, !- Report 2 Name + SensibleHeatGainSummary, !- Report 3 Name + InputVerificationandResultsSummary, !- Report 4 Name + AdaptiveComfortSummary, !- Report 5 Name + Standard62.1Summary, !- Report 6 Name + ClimaticDataSummary, !- Report 7 Name + EquipmentSummary, !- Report 8 Name + EnvelopeSummary, !- Report 9 Name + LightingSummary, !- Report 10 Name + HVACSizingSummary, !- Report 11 Name + SystemSummary, !- Report 12 Name + ComponentSizingSummary, !- Report 13 Name + OutdoorAirSummary, !- Report 14 Name + ObjectCountSummary, !- Report 15 Name + EndUseEnergyConsumptionOtherFuelsMonthly, !- Report 16 Name + PeakEnergyEndUseOtherFuelsMonthly; !- Report 17 Name + +OutputControl:Table:Style, + CommaAndHTML, !- Column Separator + JtoKWH; !- Unit Conversion + +OutputControl:ReportingTolerances, + 1.11, !- Tolerance for Time Heating Setpoint Not Met + 1.11; !- Tolerance for Time Cooling Setpoint Not Met + +OUTPUT:VARIABLE, + *, !- Key Value + Adaptive Cooling Setpoint Temperature_No Tolerance, !- Variable Name + Hourly, !- Reporting Frequency + ; !- Schedule Name + +OUTPUT:VARIABLE, + *, !- Key Value + Adaptive Heating Setpoint Temperature_No Tolerance, !- Variable Name + Hourly, !- Reporting Frequency + ; !- Schedule Name + +OUTPUT:VARIABLE, + *, !- Key Value + Zone Operative Temperature, !- Variable Name + Hourly, !- Reporting Frequency + ; !- Schedule Name + +OUTPUT:VARIABLE, + *, !- Key Value + Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature, !- Variable Name + Hourly, !- Reporting Frequency + ; !- Schedule Name + +OUTPUT:VARIABLE, + EMS, !- Key Value + Adaptive Cooling Setpoint Temperature_No Tolerance, !- Variable Name + Hourly; !- Reporting Frequency + +OUTPUT:VARIABLE, + EMS, !- Key Value + Adaptive Heating Setpoint Temperature_No Tolerance, !- Variable Name + Hourly; !- Reporting Frequency + +OUTPUT:VARIABLE, + BLOCK1:ZONE1, !- Key Value + Zone Operative Temperature, !- Variable Name + Hourly; !- Reporting Frequency + +OUTPUT:VARIABLE, + PEOPLE BLOCK1:ZONE1, !- Key Value + Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature, !- Variable Name + Hourly; !- Reporting Frequency + +OUTPUT:METER, + Heating:Electricity, !- Key Name + hourly; !- Reporting Frequency + +OUTPUT:METER, + Cooling:Electricity, !- Key Name + hourly; !- Reporting Frequency + +OUTPUT:METER, + Electricity:HVAC, !- Key Name + hourly; !- Reporting Frequency + +OUTPUT:METER, + Heating:Electricity, !- Key Name + Hourly; !- Reporting Frequency + +OUTPUT:METER, + Cooling:Electricity, !- Key Name + Hourly; !- Reporting Frequency + +OUTPUT:METER, + Electricity:HVAC, !- Key Name + Hourly; !- Reporting Frequency + +OUTPUTCONTROL:FILES, + Yes, !- Output CSV + Yes, !- Output MTR + Yes, !- Output ESO + Yes, !- Output EIO + Yes, !- Output Tabular + Yes, !- Output SQLite + Yes, !- Output JSON + Yes, !- Output AUDIT + Yes, !- Output Zone Sizing + Yes, !- Output System Sizing + Yes, !- Output DXF + Yes, !- Output BND + Yes, !- Output RDD + Yes, !- Output MDD + Yes, !- Output MTD + Yes, !- Output END + Yes, !- Output SHD + Yes, !- Output DFS + Yes, !- Output GLHE + Yes, !- Output DelightIn + Yes, !- Output DelightELdmp + Yes, !- Output DelightDFdmp + Yes, !- Output EDD + Yes, !- Output DBG + Yes, !- Output PerfLog + Yes, !- Output SLN + Yes, !- Output SCI + Yes, !- Output WRL + Yes, !- Output Screen + Yes, !- Output ExtShd + Yes; !- Output Tarcog + +EnvironmentalImpactFactors, + 0.3, !- District Heating Efficiency + 3.0, !- District Cooling COP + 0.25, !- Steam Conversion Efficiency + 80.7272, !- Total Carbon Equivalent Emission Factor From N2O + 6.2727, !- Total Carbon Equivalent Emission Factor From CH4 + 0.2727; !- Total Carbon Equivalent Emission Factor From CO2 + +Output:Diagnostics, + DisplayAdvancedReportVariables; !- Key 1 \ No newline at end of file diff --git a/accim/parametric_and_optimisation/main.py b/accim/parametric_and_optimisation/main.py index 5bce54e..524b8dd 100644 --- a/accim/parametric_and_optimisation/main.py +++ b/accim/parametric_and_optimisation/main.py @@ -555,15 +555,17 @@ def sampling_full_set(self): Combines all values from all parameters and saves it into a pandas DataFrame, stored in an internal variable named parameters_values_df. """ + from accim.parametric_and_optimisation.utils import make_all_combinations if self.descriptors_has_options: num_samples = 1 parameters_values = {} for p in self.parameters_list: num_samples = num_samples * len(p.value_descriptors[0].options) parameters_values.update({p.value_descriptors[0].name: p.value_descriptors[0].options}) - from itertools import product - combinations = list(product(*parameters_values.values())) - parameters_values_df = pd.DataFrame(combinations, columns=parameters_values.keys()) + # from itertools import product + # combinations = list(product(*parameters_values.values())) + # parameters_values_df = pd.DataFrame(combinations, columns=parameters_values.keys()) + parameters_values_df = make_all_combinations(parameters_values) else: raise KeyError('sampling_full_set method can only be used with option (i.e. category) descriptors.') @@ -765,18 +767,27 @@ def run_optimisation( self.outputs_optimisation = outputs_optimisation # return outputs_optimisation - def get_hourly_df(self): + def get_hourly_df(self, start_date: str = '2024-01-01 01'): """ - Transforms the hourly values of outputs_param_simulation to a new pandas DataFrame - named outputs_param_simulation_hourly. + Transforms the hourly values of outputs_param_simulation to a new pandas DataFrame, saved in the + internal variable named outputs_param_simulation_hourly. + :param start_date: the start date for the simulation results, in format 'YYY-MM-DD HH' """ parameter_columns = [i.name for i in self.parameters_list] parameter_columns.append('epw') - self.outputs_hourly_columns = identify_hourly_columns(self.outputs_param_simulation) - self.outputs_param_simulation_hourly = expand_to_hourly_dataframe(self.outputs_param_simulation, parameter_columns) - + self.outputs_param_simulation_hourly = expand_to_hourly_dataframe( + df=self.outputs_param_simulation, + parameter_columns=parameter_columns, + start_date=start_date + ) + def get_hourly_df_columns(self): + """ + Identifies the columns which contain hourly values, and save the names in a list, saved in the + internal variable named outputs_hourly_columns + """ + self.outputs_hourly_columns = identify_hourly_columns(self.outputs_param_simulation) class AccimPredefModelsParamSim(OptimParamSimulation): def __init__( diff --git a/accim/parametric_and_optimisation/utils.py b/accim/parametric_and_optimisation/utils.py index 8585619..cb38208 100644 --- a/accim/parametric_and_optimisation/utils.py +++ b/accim/parametric_and_optimisation/utils.py @@ -76,3 +76,15 @@ def identify_hourly_columns(df): df[col].apply(lambda x: isinstance(x, str) and x.startswith('[') and x.endswith(']')).all() ] return hourly_columns + +def make_all_combinations(parameters_values_dict: dict) -> pd.DataFrame: + """ + Takes all values from all the parameters and return a pandas DataFrame with all possible combinations. + + :param parameters_values_dict: a dictionary in the format {'parameter name': list_of_values} + :return: a pandas DataFrame with all possible combinations + """ + from itertools import product + combinations = list(product(*parameters_values_dict.values())) + parameters_values_df = pd.DataFrame(combinations, columns=parameters_values_dict.keys()) + return parameters_values_df diff --git a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/accim_predefined_model-checkpoint.ipynb b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/accim_predefined_model-checkpoint.ipynb index bc7833a..cb2d444 100644 --- a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/accim_predefined_model-checkpoint.ipynb +++ b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/accim_predefined_model-checkpoint.ipynb @@ -38,6 +38,7 @@ "from accim.parametric_and_optimisation.objectives import return_time_series\n", "from besos import eppy_funcs as ef\n", "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", "from accim.utils import print_available_outputs_mod\n", "from accim.parametric_and_optimisation.main import OptimParamSimulation, get_rdd_file_as_df, get_mdd_file_as_df, parse_mtd_file\n", "from os import listdir\n" @@ -180,17 +181,17 @@ "=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================\n", "\n", "Starting with file:\n", - "The occupied zones in the model are:\n", + "The occupied zones in the model are:\n", "BLOCK1:ZONE2\n", "BLOCK1:ZONE1\n", - "The windows and doors in the model are:\n", + "The windows and doors in the model are:\n", "Block1_Zone2_Wall_3_0_0_0_0_0_Win\n", "Block1_Zone2_Wall_4_0_0_0_0_0_Win\n", "Block1_Zone2_Wall_5_0_0_0_0_0_Win\n", "Block1_Zone1_Wall_2_0_0_0_0_0_Win\n", "Block1_Zone1_Wall_3_0_0_0_0_0_Win\n", "Block1_Zone1_Wall_5_0_0_0_0_0_Win\n", - "The zones in the model are:\n", + "The zones in the model are:\n", "BLOCK1_ZONE2\n", "BLOCK1_ZONE1\n", "The people objects in the model have been amended.\n", @@ -1755,7 +1756,7 @@ " EMS\n", " Adaptive Cooling Setpoint Temperature_No Toler...\n", " Hourly\n", - " <function return_time_series at 0x000002427114...\n", + " <function return_time_series at 0x00000267CB9B...\n", " Adaptive Cooling Setpoint Temperature_No Toler...\n", " \n", " \n", @@ -1763,7 +1764,7 @@ " EMS\n", " Adaptive Heating Setpoint Temperature_No Toler...\n", " Hourly\n", - " <function return_time_series at 0x000002427114...\n", + " <function return_time_series at 0x00000267CB9B...\n", " Adaptive Heating Setpoint Temperature_No Toler...\n", " \n", " \n", @@ -1771,7 +1772,7 @@ " BLOCK1:ZONE1\n", " Zone Operative Temperature\n", " Hourly\n", - " <function return_time_series at 0x000002427114...\n", + " <function return_time_series at 0x00000267CB9B...\n", " Zone Operative Temperature_time series\n", " \n", " \n", @@ -1779,7 +1780,7 @@ " PEOPLE BLOCK1:ZONE1\n", " Zone Thermal Comfort ASHRAE 55 Adaptive Model ...\n", " Hourly\n", - " <function return_time_series at 0x000002427114...\n", + " <function return_time_series at 0x00000267CB9B...\n", " Zone Thermal Comfort ASHRAE 55 Adaptive Model ...\n", " \n", " \n", @@ -1794,10 +1795,10 @@ "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", "\n", " frequency func \\\n", - "0 Hourly , key_name='Heating:Electricity'),\n", - " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", - " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", - " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", - " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", - " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", - " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" + "[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'),\n", + " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", + " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", + " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", + " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1892,7 +1893,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "id": "004029fa-ff72-4f43-a9c3-e004757463e4", "metadata": {}, "outputs": [ @@ -1918,7 +1919,7 @@ " 'ASTtol']" ] }, - "execution_count": 29, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1940,7 +1941,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "id": "b4eb43dd-879b-4175-ab56-e9c04ea342af", "metadata": {}, "outputs": [ @@ -1976,19 +1977,19 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "id": "4b72a5b5-417f-410b-b30a-bf6550dbb364", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]),\n", - " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]),\n", - " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])]" + "[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])]" ] }, - "execution_count": 31, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2023,7 +2024,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "id": "cd2c3ebc-99ea-46a2-94cf-37b3ddd77630", "metadata": {}, "outputs": [], @@ -2041,17 +2042,17 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "id": "a4f80f41-febc-47e0-b610-8c07893a5c20", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" + "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" ] }, - "execution_count": 33, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2078,7 +2079,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 30, "id": "30f78385-766d-47f2-b394-c922f8403b30", "metadata": {}, "outputs": [ @@ -2131,7 +2132,7 @@ "1 2 90 3" ] }, - "execution_count": 38, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2153,7 +2154,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 31, "id": "286da8cc-4ee2-41b3-9d3a-07ba98f708e0", "metadata": {}, "outputs": [], @@ -2171,7 +2172,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 32, "id": "dfebdd41-ee54-4fcf-ab7e-12db7e9461fb", "metadata": {}, "outputs": [ @@ -2224,7 +2225,7 @@ "3 2 90 3" ] }, - "execution_count": 40, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -2233,21 +2234,815 @@ "parametric.parameters_values_df" ] }, + { + "cell_type": "markdown", + "id": "c0c976c6-eed1-4727-a274-975585466130", + "metadata": {}, + "source": [ + "### Running the simulations" + ] + }, + { + "cell_type": "markdown", + "id": "302f66b3-55ba-468b-91bb-d0c7a53f4387", + "metadata": {}, + "source": [ + "Now, we're ready to run the simulations, by means of the `run_parametric_simulation(epws, out_dir, df, processes)` method. After calling the method, the outputs (a DataFrame) is saved in the internal variable `outputs_param_simulation`. It is based on the use of the `EvaluatorEP` class, `df_apply` method. We want to run the parametric simulations with both Sydney and Seville climate files, therefore the filenames are input in a list in the `epws` argument. The simulation outputs will be saved in a directory named 'notebook_temp_dir'. The values for the parameters will be driven by the internal variable `parameters_values_df`, as input in the `df` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5e5a19ab-d2a3-44ac-9d2d-01672ac8db08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n" + ] + } + ], + "source": [ + "parametric.run_parametric_simulation(\n", + " epws=['Sydney.epw', 'Seville.epw'],\n", + " out_dir='notebook_temp_dir',\n", + " df=parametric.parameters_values_df,\n", + " #processes=6, # The number of CPUs to be used. Default is 2.\n", + " #keep_input=True, # To keep the input values of parameters, as entered in df argument. Default is True.\n", + " #keep_dirs=True # To keep the simulation outputs. Default is True.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8f768acb-e813-4d4d-91d7-7b96a98ecaad", + "metadata": {}, + "source": [ + "Let's take a look at the simulation results" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "0cff32f0-5734-4e0b-a740-5af75ec6cd42", + "execution_count": 34, + "id": "9461f205-acd1-4532-b691-9dd7d9778873", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexComfStandCATComfModHeating:ElectricityCooling:ElectricityElectricity:HVACAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time seriesoutput_direpw
0228032.593138e+091.754898e+102.735351e+10[27.325804398148147, 27.91096527777778, 27.910...[20.325804398148147, 20.910965277777777, 20.91...[23.771739074031462, 23.897364637263966, 23.75...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8...Sydney
1329033.639536e+093.621666e+104.728630e+10[26.325804398148147, 26.91096527777778, 26.910...[21.325804398148147, 21.910965277777777, 21.91...[23.66668667903001, 23.8347876559133, 23.82938...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH...Sydney
2228032.062257e+093.171532e+104.054615e+10[24.785096064814812, 24.86211527777778, 24.862...[17.785096064814812, 17.86211527777778, 17.862...[21.151988618198942, 21.208271993109328, 20.26...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR...Seville
3329032.297988e+094.516874e+105.445162e+10[23.785096064814812, 23.86211527777778, 23.862...[18.785096064814812, 18.86211527777778, 18.862...[22.631374958463137, 20.71719912972282, 20.492...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H...Seville
\n", + "
" + ], + "text/plain": [ + " index ComfStand CAT ComfMod Heating:Electricity Cooling:Electricity \\\n", + "0 2 2 80 3 2.593138e+09 1.754898e+10 \n", + "1 3 2 90 3 3.639536e+09 3.621666e+10 \n", + "2 2 2 80 3 2.062257e+09 3.171532e+10 \n", + "3 3 2 90 3 2.297988e+09 4.516874e+10 \n", + "\n", + " Electricity:HVAC \\\n", + "0 2.735351e+10 \n", + "1 4.728630e+10 \n", + "2 4.054615e+10 \n", + "3 5.445162e+10 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 [27.325804398148147, 27.91096527777778, 27.910... \n", + "1 [26.325804398148147, 26.91096527777778, 26.910... \n", + "2 [24.785096064814812, 24.86211527777778, 24.862... \n", + "3 [23.785096064814812, 23.86211527777778, 23.862... \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 [20.325804398148147, 20.910965277777777, 20.91... \n", + "1 [21.325804398148147, 21.910965277777777, 21.91... \n", + "2 [17.785096064814812, 17.86211527777778, 17.862... \n", + "3 [18.785096064814812, 18.86211527777778, 18.862... \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 [23.771739074031462, 23.897364637263966, 23.75... \n", + "1 [23.66668667903001, 23.8347876559133, 23.82938... \n", + "2 [21.151988618198942, 21.208271993109328, 20.26... \n", + "3 [22.631374958463137, 20.71719912972282, 20.492... \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \\\n", + "0 [21.325694444444437, 21.325694444444437, 21.32... \n", + "1 [21.325694444444437, 21.325694444444437, 21.32... \n", + "2 [11.490694444444445, 11.490694444444445, 11.49... \n", + "3 [11.490694444444445, 11.490694444444445, 11.49... \n", + "\n", + " output_dir epw \n", + "0 notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8... Sydney \n", + "1 notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH... Sydney \n", + "2 notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR... Seville \n", + "3 notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H... Seville " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.outputs_param_simulation" + ] + }, + { + "cell_type": "markdown", + "id": "efa37b3d-45f9-4bbd-b2c2-3f36b743ee0b", + "metadata": {}, + "source": [ + "We can see the columns are the following:" + ] + }, + { + "cell_type": "markdown", + "id": "907ca41e-e151-4e90-a37b-ed0b596e1852", + "metadata": {}, + "source": [ + "- the parameters, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c733f7a2-fd24-4b14-9ab1-98075d261401", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand', 'CAT', 'ComfMod']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.value_descriptors[0].name for i in parametric.parameters_list]" + ] + }, + { + "cell_type": "markdown", + "id": "c30f391f-08a0-488e-8dd3-67aa9189790c", + "metadata": {}, + "source": [ + "- the outputs, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "405d7408-599d-4493-a497-5bfe686db381", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + " 'Adaptive Cooling Setpoint Temperature_No Tolerance_time series',\n", + " 'Adaptive Heating Setpoint Temperature_No Tolerance_time series',\n", + " 'Zone Operative Temperature_time series',\n", + " 'Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.name for i in parametric.sim_outputs]" + ] + }, + { + "cell_type": "markdown", + "id": "cf172d66-6883-481c-9779-a3382616924e", + "metadata": {}, + "source": [ + "- the path to the output files for each simulation, in the column 'out_dir'\n", + "- the epw for each simulation, in the column 'epw'" + ] + }, + { + "cell_type": "markdown", + "id": "e2f6302e-8105-482b-a984-c4bc03ac8086", + "metadata": {}, + "source": [ + "### Visualising the results" + ] + }, + { + "cell_type": "markdown", + "id": "8de1cbd1-8cf0-41c4-b394-6cf8481f2edd", + "metadata": {}, + "source": [ + "#### Aggregated columns" + ] + }, + { + "cell_type": "markdown", + "id": "d7b2ad11-4f2b-46d0-8a59-3e7e842b2ae9", + "metadata": {}, + "source": [ + "At this point, if you have some knowledge of pandas and some package to plot the data (e.g. matplotlib or seaborn), you can carry out your own analysis and visualization. We're going to do some example below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3b08c431-5c10-47bc-b8d9-bbed174fb35c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(\n", + " data=parametric.outputs_param_simulation,\n", + " x='Heating:Electricity',\n", + " y='Cooling:Electricity',\n", + " hue='CAT',\n", + " style='epw'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "710213aa-c8d2-4565-b121-a0db80753b52", + "metadata": {}, + "source": [ + "#### Time series columns" + ] + }, + { + "cell_type": "markdown", + "id": "d66994c0-7514-456f-8d9c-2c3acce6e656", + "metadata": {}, + "source": [ + "If you requested some output to be reported in time series, you can get a dataframe containing the hourly values using the method `get_hourly_df()`. This dataframe is saved in the internal variable `outputs_param_simulation_hourly`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "74848927-55f4-4e27-ae75-bf66baf49ba7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfModepwhourdatetimeAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series
02803Sydney12024-01-01 01:00:0027.32580420.32580423.77173921.325694
12803Sydney22024-01-01 02:00:0027.91096520.91096523.89736521.325694
22803Sydney32024-01-01 03:00:0027.91096520.91096523.75949921.325694
32803Sydney42024-01-01 04:00:0027.91096520.91096523.81012121.325694
42803Sydney52024-01-01 05:00:0027.91096520.91096524.11513121.325694
.................................
350352903Seville87562024-12-30 20:00:0023.93343018.93343023.61795711.720741
350362903Seville87572024-12-30 21:00:0023.93343018.93343023.57101211.720741
350372903Seville87582024-12-30 22:00:0023.93343018.93343023.34048011.720741
350382903Seville87592024-12-30 23:00:0023.93343018.93343023.06050111.720741
350392903Seville87602024-12-31 00:00:0023.93343018.93343022.83835211.720741
\n", + "

35040 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " ComfStand CAT ComfMod epw hour datetime \\\n", + "0 2 80 3 Sydney 1 2024-01-01 01:00:00 \n", + "1 2 80 3 Sydney 2 2024-01-01 02:00:00 \n", + "2 2 80 3 Sydney 3 2024-01-01 03:00:00 \n", + "3 2 80 3 Sydney 4 2024-01-01 04:00:00 \n", + "4 2 80 3 Sydney 5 2024-01-01 05:00:00 \n", + "... ... ... ... ... ... ... \n", + "35035 2 90 3 Seville 8756 2024-12-30 20:00:00 \n", + "35036 2 90 3 Seville 8757 2024-12-30 21:00:00 \n", + "35037 2 90 3 Seville 8758 2024-12-30 22:00:00 \n", + "35038 2 90 3 Seville 8759 2024-12-30 23:00:00 \n", + "35039 2 90 3 Seville 8760 2024-12-31 00:00:00 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 27.325804 \n", + "1 27.910965 \n", + "2 27.910965 \n", + "3 27.910965 \n", + "4 27.910965 \n", + "... ... \n", + "35035 23.933430 \n", + "35036 23.933430 \n", + "35037 23.933430 \n", + "35038 23.933430 \n", + "35039 23.933430 \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 20.325804 \n", + "1 20.910965 \n", + "2 20.910965 \n", + "3 20.910965 \n", + "4 20.910965 \n", + "... ... \n", + "35035 18.933430 \n", + "35036 18.933430 \n", + "35037 18.933430 \n", + "35038 18.933430 \n", + "35039 18.933430 \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 23.771739 \n", + "1 23.897365 \n", + "2 23.759499 \n", + "3 23.810121 \n", + "4 24.115131 \n", + "... ... \n", + "35035 23.617957 \n", + "35036 23.571012 \n", + "35037 23.340480 \n", + "35038 23.060501 \n", + "35039 22.838352 \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \n", + "0 21.325694 \n", + "1 21.325694 \n", + "2 21.325694 \n", + "3 21.325694 \n", + "4 21.325694 \n", + "... ... \n", + "35035 11.720741 \n", + "35036 11.720741 \n", + "35037 11.720741 \n", + "35038 11.720741 \n", + "35039 11.720741 \n", + "\n", + "[35040 rows x 10 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.get_hourly_df()\n", + "parametric.outputs_param_simulation_hourly" + ] + }, + { + "cell_type": "markdown", + "id": "dbac2c53-1b49-43a5-b8c7-02877ec8b4aa", + "metadata": {}, + "source": [ + "Let's prepare the `outputs_param_simulation_hourly` df for plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "921324d1-f102-4bd3-b43e-340e435bdca0", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#Let's make a copy of the dataframe to not to modify the original one\n", + "df = parametric.outputs_param_simulation_hourly.copy()\n", + "\n", + "# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot:\n", + "rmot = [i for i in df.columns if 'Running Average' in i][0]\n", + "\n", + "#Let's remove the columns where value is the same for all rows\n", + "for c in df.columns:\n", + " if len(set(df[c])) == 1:\n", + " df = df.drop(columns=[c])\n", + "#Now let's remove the hour and datetime columns, since will\n", + "df = df.drop(columns=['hour'])\n", + "\n", + "# Now let's reshape the df for plotting purposes\n", + "df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4aa2a50-88ee-4c66-ab3a-48a26eaec9c1", + "metadata": {}, + "source": [ + "Firstly, we're going to plot the hourly temperatures to see the slope of the comfort model, and check all hours are within thermal comfort limits (since we applied adaptive setpoints)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2213ebd0-2f3c-4e74-bd06-117ec72c3108", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.scatterplot,\n", + " x=rmot,\n", + " y='value',\n", + " hue='variable',\n", + " s=1,\n", + " #alpha=0.5\n", + ")\n", + "g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "\n", + "for lh in g._legend.legend_handles:\n", + " lh.set_markersize(5)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "14dfbd82-2e4f-45b6-96ca-da72265e117c", + "metadata": {}, + "source": [ + "Now, we're going to plot time on x-axis and change the plot type to lineplot, to see the variarion of the indoor operative temperature throughout the year:" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "e74f659a-6719-4728-a4a7-628dfbab4f75", + "execution_count": 44, + "id": "b673ca88-bbd8-4c89-989e-7cf7c613e90c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.lineplot,\n", + " x='datetime',\n", + " y='value',\n", + " hue='variable',\n", + ")\n", + "g.set_axis_labels('Time', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0b253ef-b8ee-402b-a741-b4b89181e589", + "metadata": {}, + "source": [ + "We're done with the example, so let's remove all new files, so that we can re-run it again." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "abefda96-72f4-4e8a-8d2a-5337647c480b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'available_outputs', 'notebook_temp_dir'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_files = [i for i in listdir()]\n", + "new_files = set(current_files) - set(original_files)\n", + "new_files" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f9dbc462-7f2b-40e2-b7b0-41e9243b3b59", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import os\n", + "import shutil\n", + "for item in new_files:\n", + " item_path = os.path.join(os.getcwd(), item)\n", + " if os.path.isfile(item_path):\n", + " os.remove(item_path)\n", + " elif os.path.isdir(item_path):\n", + " shutil.rmtree(item_path)" + ] } ], "metadata": { diff --git a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/param_sim_accim_custom_model-checkpoint.ipynb b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/param_sim_accim_custom_model-checkpoint.ipynb new file mode 100644 index 0000000..41dc181 --- /dev/null +++ b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/param_sim_accim_custom_model-checkpoint.ipynb @@ -0,0 +1,6316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2cf45ee0-4144-4034-99a9-311270c74d30", + "metadata": {}, + "source": [ + "# Parametric simulation using accim custom models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f71c6322-10f1-4fde-bc8d-358163b8619b", + "metadata": {}, + "outputs": [], + "source": [ + "#todo import qgrid to manually change output dfs" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a8361222-b96d-4121-9560-1480e3ee4f44", + "metadata": {}, + "outputs": [], + "source": [ + "import accim\n", + "from accim.parametric_and_optimisation.objectives import return_time_series\n", + "from accim.parametric_and_optimisation.utils import make_all_combinations\n", + "from besos import eppy_funcs as ef\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "from accim.utils import print_available_outputs_mod\n", + "from accim.parametric_and_optimisation.main import OptimParamSimulation, get_rdd_file_as_df, get_mdd_file_as_df, parse_mtd_file\n", + "from os import listdir\n" + ] + }, + { + "cell_type": "markdown", + "id": "c699ceba-f765-47c5-b5fd-b0cee5522b3a", + "metadata": {}, + "source": [ + "Let's have a look at the files we currently have in the path:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "233674c3-f15d-4917-aa47-430ae91486d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.ipynb_checkpoints',\n", + " 'param_sim_accim_custom_model.ipynb',\n", + " 'param_sim_accim_predefined_model.ipynb',\n", + " 'Seville.epw',\n", + " 'Sydney.epw',\n", + " 'TestModel.idf',\n", + " '__init__.py']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "original_files = [i for i in listdir()]\n", + "original_files" + ] + }, + { + "cell_type": "markdown", + "id": "16e31d87-3a87-40c1-aeef-d6ea1171291a", + "metadata": {}, + "source": [ + "Firstly, the IDF must be read using besos's `get_building` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d6e13f6-7466-4e96-a559-8c771895c1ac", + "metadata": {}, + "outputs": [], + "source": [ + "building = ef.get_building('TestModel.idf')" + ] + }, + { + "cell_type": "markdown", + "id": "affcedd2-3206-4178-bb93-3ae151e04301", + "metadata": {}, + "source": [ + "For this analysis, we want to use the HVAC system in all hours of the year, so that temperature is always comfortable. Therefore, we are going to set the occupancy to always on by means of the function `accim.utils.set_occupancy_to_always`, in which we input the IDF class instance we read in the previous cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f5c2feb1-ca1e-4b8a-bc50-1f116cca9fa4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On 24/7 Schedule:Compact object was already in the model.\n", + "People Block1:Zone2 Number of People Schedule Name has been set to always occupied.\n", + "People Block1:Zone1 Number of People Schedule Name has been set to always occupied.\n" + ] + } + ], + "source": [ + "accim.utils.set_occupancy_to_always(idf_object=building)" + ] + }, + { + "cell_type": "markdown", + "id": "29827b0f-9086-4e26-873b-db2f7818a18a", + "metadata": {}, + "source": [ + "Now, let's start with the settings for the parametric analysis. First, let's instantiate the class `OptimParamSimulation`, and let's pass the IDF instance in the argument `building`. Argument `parameters_type` can take 3 different strings:\n", + "- \"accim predefined model\", in which models are those previously defined in accim (ComfStand=0 to ComfStand=22);\n", + "- \"accim custom model\", in which key parameters of the adaptive comfort model are defined in the relevant arguments;\n", + "- \"apmv setpoints\", in which setpoints are based on the aPMV (Adaptive Predicted Mean Vote) instead of the PMV index;\n", + "\n", + "In this case, we're going to use the 'accim custom model' type, in which we can define the adaptive comfort model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2a5fa261-bfee-4b08-a8fe-c62f46a481eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------\n", + "Adaptive-Comfort-Control-Implemented Model (ACCIM) v0.7.5\n", + "--------------------------------------------------------\n", + "\n", + "This tool allows to apply adaptive setpoint temperatures. \n", + "For further information, please read the documentation: \n", + "https://accim.readthedocs.io/en/master/\n", + "For a visual understanding of the tool, please visit the following jupyter notebooks:\n", + "- Using addAccis() to apply adaptive setpoint temperatures\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/addAccis/using_addAccis.html\n", + "- Using rename_epw_files() to rename the EPWs for proper data analysis after simulation\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/rename_epw_files/using_rename_epw_files.html\n", + "- Using runEp() to directly run simulations with EnergyPlus\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/runEp/using_runEp.html\n", + "- Using the class Table() for data analysis\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/Table/using_Table.html\n", + "- Full example\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/full_example/full_example.html\n", + "\n", + "Starting with the process.\n", + "Basic input data:\n", + "ScriptType is: vrf_mm\n", + "Supply Air Temperature Input Method is: temperature difference\n", + "Output type is: standard\n", + "Output frequencies are: \n", + "['hourly']\n", + "EnergyPlus version is: 23.1\n", + "Temperature Control method is: temperature\n", + "\n", + "=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================\n", + "\n", + "Starting with file:\n", + "The occupied zones in the model are:\n", + "BLOCK1:ZONE2\n", + "BLOCK1:ZONE1\n", + "The windows and doors in the model are:\n", + "Block1_Zone2_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_4_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_5_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_2_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_5_0_0_0_0_0_Win\n", + "The zones in the model are:\n", + "BLOCK1_ZONE2\n", + "BLOCK1_ZONE1\n", + "The people objects in the model have been amended.\n", + "BLOCK1:ZONE2 Thermostat has been added\n", + "BLOCK1:ZONE1 Thermostat has been added\n", + "On Schedule already was in the model\n", + "TypOperativeTempControlSch Schedule already was in the model\n", + "All ZoneHVAC:IdealLoadsAirSystem Heating and Cooling availability schedules has been set to on\n", + "On 24/7 Schedule already was in the model\n", + "Control type schedule: Always 4 Schedule has been added\n", + "Relative humidity setpoint schedule: Always 50.00 Schedule has been added\n", + "Heating Fanger comfort setpoint: Always -0.5 Schedule has been added\n", + "Cooling Fanger comfort setpoint: Always 0.1 Schedule has been added\n", + "Zone CO2 setpoint: Always 900ppm Schedule has been added\n", + "Min CO2 concentration: Always 600ppm Schedule has been added\n", + "Generic contaminant setpoint: Always 0.5ppm Schedule has been added\n", + "Air distribution effectiveness (always 1) Schedule has been added\n", + "VRF Heating Cooling (Northern Hemisphere) Schedule has been added\n", + "DefaultFanEffRatioCurve Curve:Cubic Object has been added\n", + "VRFTUCoolCapFT Curve:Cubic Object has been added\n", + "VRFTUHeatCapFT Curve:Cubic Object has been added\n", + "VRFCoolCapFTBoundary Curve:Cubic Object has been added\n", + "VRFCoolEIRFTBoundary Curve:Cubic Object has been added\n", + "CoolingEIRLowPLR Curve:Cubic Object has been added\n", + "VRFHeatCapFTBoundary Curve:Cubic Object has been added\n", + "VRFHeatEIRFTBoundary Curve:Cubic Object has been added\n", + "HeatingEIRLowPLR Curve:Cubic Object has been added\n", + "DefaultFanPowerRatioCurve Curve:Exponent Object has been added\n", + "DXHtgCoilDefrostEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFTHi Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatCapFT Curve:Biquadratic Object has been added\n", + "VRFHeatCapFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFT Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFTHi Curve:Biquadratic Object has been added\n", + "CoolingLengthCorrectionFactor Curve:Biquadratic Object has been added\n", + "VRF Piping Correction Factor for Length in Heating Mode Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Energy Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Energy Modifier Curve:Biquadratic Object has been added\n", + "VRFACCoolCapFFF Curve:Quadratic Object has been added\n", + "CoolingEIRHiPLR Curve:Quadratic Object has been added\n", + "VRFCPLFFPLR Curve:Quadratic Object has been added\n", + "HeatingEIRHiPLR Curve:Quadratic Object has been added\n", + "CoolingCombRatio Curve:Linear Object has been added\n", + "HeatingCombRatio Curve:Linear Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Zone List Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Zone List Object has been added\n", + "BLOCK1:ZONE2 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE1 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE2 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE1 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE2 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE1 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE2 Nodelist Objects has been added\n", + "BLOCK1:ZONE1 Nodelist Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Fan:ConstantVolume Object has been added\n", + "BLOCK1:ZONE1 Fan:ConstantVolume Object has been added\n", + "Vent_SP_temp Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "Added - SetComfTemp Program\n", + "Added - CountHours_BLOCK1_ZONE2 Program\n", + "Added - CountHours_BLOCK1_ZONE1 Program\n", + "Added - SetAppLimits Program\n", + "Added - ApplyCAT Program\n", + "Added - SetAST Program\n", + "Added - SetASTnoTol Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program\n", + "Added - SetInputData Program\n", + "Added - SetVOFinputData Program\n", + "Added - SetVST Program\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - Comfort Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Output Variable\n", + "Added - z_test_ComfStand Output Variable\n", + "Added - z_test_ACSTaul Output Variable\n", + "Added - z_test_ACSTall Output Variable\n", + "Added - z_test_AHSTaul Output Variable\n", + "Added - z_test_AHSTall Output Variable\n", + "Added - z_test_CAT Output Variable\n", + "Added - z_test_ACSToffset Output Variable\n", + "Added - z_test_AHSToffset Output Variable\n", + "Added - z_test_ComfMod Output Variable\n", + "Added - z_test_ACSTtol Output Variable\n", + "Added - z_test_SetpointAcc Output Variable\n", + "Added - z_test_CustAST_m Output Variable\n", + "Added - z_test_CustAST_n Output Variable\n", + "Added - z_test_AHSTtol Output Variable\n", + "Added - Ventilation Setpoint Temperature Output Variable\n", + "Added - Minimum Outdoor Temperature for ventilation Output Variable\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Multiplier for Ventilation Opening Factor Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Output Variable\n", + "Global variables objects have been added\n", + "Internal variables objects have been added\n", + "Added - RMOT Sensor\n", + "Added - PMOT Sensor\n", + "Added - Occ_count_BLOCK1_ZONE2 Sensor\n", + "Added - Occ_count_BLOCK1_ZONE1 Sensor\n", + "Added - BLOCK1_ZONE2_OpT Sensor\n", + "Added - BLOCK1_ZONE2_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE2_OutT Sensor\n", + "Added - BLOCK1_ZONE1_OpT Sensor\n", + "Added - BLOCK1_ZONE1_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE1_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - OutT Sensor\n", + "Added - AHST_Act_BLOCK1_ZONE2 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE2 Actuator\n", + "Added - AHST_Act_BLOCK1_ZONE1 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE1 Actuator\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - BLOCK1_ZONE2_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE2_HeatCoil Sensor\n", + "Added - BLOCK1_ZONE1_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE1_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - SetComfTemp Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetAppLimits Program Calling Manager\n", + "Added - ApplyCAT Program Calling Manager\n", + "Added - SetAST Program Calling Manager\n", + "Added - SetASTnoTol Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetInputData Program Calling Manager\n", + "Added - SetVOFinputData Program Calling Manager\n", + "Added - SetVST Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - Comfort Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfStand Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CAT Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfMod Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_SetpointAcc Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_m Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_n Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Multiplier for Ventilation Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Cooling Coil Total Cooling Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Heating Coil Heating Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electric Demand Power Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electricity Demand Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Surface Venting Window or Door Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Drybulb Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Wind Speed Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Relative Humidity Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Cooling Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Heating Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - OutputControl:Files object\n", + "Added - Output:VariableDictionary object\n", + "\n", + "=======================END OF OUTPUT IDF FILE GENERATION PROCESS=======================\n", + "\n" + ] + } + ], + "source": [ + "parametric = OptimParamSimulation(\n", + " building=building,\n", + " parameters_type='accim custom model',\n", + " #output_type='standard', #\n", + " #output_keep_existing=False, #\n", + " #output_freqs=['hourly'], #\n", + " #ScriptType='vrf_mm', #\n", + " #SupplyAirTempInputMethod='temperature difference', #\n", + " #debugging=True, #\n", + " #verbosemode=False #\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51eed0eb-7b3a-4428-8952-070a650acf61", + "metadata": {}, + "source": [ + "An initial and generic version of the Adaptive-Comfort-Control-Implementation Script (ACCIS) has been added to the idf instance `building`. For instance, you can take a look at the parameter values accis currently has:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad387b77-4998-4e51-979a-928fa52e551d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetInputData, !- Name\n", + " set ComfStand = 1, !- Program Line 1\n", + " set CAT = 1, !- Program Line 2\n", + " set ComfMod = 2, !- Program Line 3\n", + " set HVACmode = 2, !- Program Line 4\n", + " set VentCtrl = 0, !- Program Line 5\n", + " set VSToffset = 0, !- Program Line 6\n", + " set MinOToffset = 7, !- Program Line 7\n", + " set MaxWindSpeed = 6, !- Program Line 8\n", + " set ACSTtol = -0.25, !- Program Line 9\n", + " set AHSTtol = 0.25, !- Program Line 10\n", + " set CoolSeasonStart = 121, !- Program Line 11\n", + " set CoolSeasonEnd = 274; !- Program Line 12]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata']" + ] + }, + { + "cell_type": "markdown", + "id": "99605e82-4d01-4aee-835c-ab9c6c66faa4", + "metadata": {}, + "source": [ + "## Setting the outputs" + ] + }, + { + "cell_type": "markdown", + "id": "8d3f905b-543d-4f32-b0dd-67ed99f77780", + "metadata": {}, + "source": [ + "**If you have already read any of the other parametric simulation examples, you can skip this entire outputs section, since it is exactly the same.**" + ] + }, + { + "cell_type": "markdown", + "id": "bc714539-79c3-46e9-9caa-44a21fc413cb", + "metadata": {}, + "source": [ + "### Outputs for the idf (i.e. the outputs for each simulation run)" + ] + }, + { + "cell_type": "markdown", + "id": "764b7626-0e63-4317-8bbf-28ac2fe68f68", + "metadata": {}, + "source": [ + "First of all, we are going to set the outputs of the simulations that are going to be performed. This is an important step, especially if you are going to run hundreds or thousands of simulations." + ] + }, + { + "cell_type": "markdown", + "id": "b2f1d34a-dc0a-4aac-b01f-a7425b95090f", + "metadata": {}, + "source": [ + "Let's take a look at the Output:Variable objects we currently have in the idf. The method `get_output_var_df_from_idf()` returns a pandas DataFrame which contains the information of the existing Output:Variable objects in the idf:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "12ee78ba-c60b-4ef1-a6fb-51086a8a53fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
0*Comfort TemperatureHourly
1*Adaptive Cooling Setpoint TemperatureHourly
2*Adaptive Heating Setpoint TemperatureHourly
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
...............
72*VRF Heat Pump Heating Electricity EnergyHourly
73BLOCK1_ZONE2 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
74BLOCK1_ZONE2 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
75BLOCK1_ZONE1 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
76BLOCK1_ZONE1 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
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" + ], + "text/plain": [ + " key_value \\\n", + "0 * \n", + "1 * \n", + "2 * \n", + "3 * \n", + "4 * \n", + ".. ... \n", + "72 * \n", + "73 BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil \n", + "74 BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil \n", + "75 BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil \n", + "76 BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil \n", + "\n", + " variable_name reporting_frequency \\\n", + "0 Comfort Temperature Hourly \n", + "1 Adaptive Cooling Setpoint Temperature Hourly \n", + "2 Adaptive Heating Setpoint Temperature Hourly \n", + "3 Adaptive Cooling Setpoint Temperature_No Toler... Hourly \n", + "4 Adaptive Heating Setpoint Temperature_No Toler... Hourly \n", + ".. ... ... \n", + "72 VRF Heat Pump Heating Electricity Energy Hourly \n", + "73 Cooling Coil Total Cooling Rate Hourly \n", + "74 Heating Coil Heating Rate Hourly \n", + "75 Cooling Coil Total Cooling Rate Hourly \n", + "76 Heating Coil Heating Rate Hourly \n", + "\n", + " schedule_name \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + ".. ... \n", + "72 \n", + "73 \n", + "74 \n", + "75 \n", + "76 \n", + "\n", + "[77 rows x 4 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = parametric.get_output_var_df_from_idf()\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "ab1e81e8-c2f0-40c0-b868-0cf7ddb9954f", + "metadata": {}, + "source": [ + "now, let's see the Output:Meter objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "94c9844b-aacf-41fa-b1d4-ac7b491aa14e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [key_name, frequency]\n", + "Index: []" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "093bdcab-a1aa-492a-8864-d2e4e1a0b72c", + "metadata": {}, + "source": [ + "In this case, we can see there is no Output:Meter. However, there is a large number of Output:Variable objects which might result in heavy simulation outputs. So, let's get rid of some of them. We can drop the rows we want, and then input the modified DataFrame in the method `set_output_var_df_to_idf(outputs_df)`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9ccc7b08-119c-4f4c-bb45-9aac8201fe7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
52*Zone Operative TemperatureHourly
54*Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "3 * Adaptive Cooling Setpoint Temperature_No Toler... \n", + "4 * Adaptive Heating Setpoint Temperature_No Toler... \n", + "52 * Zone Operative Temperature \n", + "54 * Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " reporting_frequency schedule_name \n", + "3 Hourly \n", + "4 Hourly \n", + "52 Hourly \n", + "54 Hourly " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = df_output_variables_idf[\n", + " (\n", + " df_output_variables_idf['variable_name'].str.contains('Setpoint Temperature_No Tolerance')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Operative Temperature')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')\n", + " )\n", + "]\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "643151b4-a20d-4d00-a1c9-5a78a56be916", + "metadata": {}, + "source": [ + "Let's keep only the Output:Variable objects we have filtered using the `set_output_var_df_to_idf(outputs_df)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8958bc66-f42d-4fc0-9622-f2b3ffb03852", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf)" + ] + }, + { + "cell_type": "markdown", + "id": "eacd9d6d-7fa1-4e6d-8bab-edbfa322140b", + "metadata": {}, + "source": [ + "We have removed all rows except the adaptive heating and cooling setpoints, the operative temperature and the running mean outdoor temperature. Next optional step is adding Output:Meter objects. We can do that using the method `set_output_met_objects_to_idf(output_meters)`, where `output_meters` is a list of Output:Meter key names." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "addf36d3-b7c2-4254-bec5-322850a464d1", + "metadata": {}, + "outputs": [], + "source": [ + "output_meters = [\n", + " 'Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + "]\n", + "parametric.set_output_met_objects_to_idf(output_meters=output_meters)" + ] + }, + { + "cell_type": "markdown", + "id": "fa66a111-e72e-4ebd-8110-8222191d6c5d", + "metadata": {}, + "source": [ + "Let's see Output:Meter objects we currently have after adding these:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "41878d11-6dcc-4158-9ec4-a74aa9d2dafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
0Heating:Electricityhourly
1Cooling:Electricityhourly
2Electricity:HVAChourly
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" + ], + "text/plain": [ + " key_name frequency\n", + "0 Heating:Electricity hourly\n", + "1 Cooling:Electricity hourly\n", + "2 Electricity:HVAC hourly" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "90fd8d9b-ecd8-4c79-9f37-775f2c2921ed", + "metadata": {}, + "source": [ + "### Outputs to be read and shown in the parametric simulation or optimisation" + ] + }, + { + "cell_type": "markdown", + "id": "bb18fd43-e07e-42f1-ac75-020bcbe96d9a", + "metadata": {}, + "source": [ + "To successfully run the parametric simulation or optimisation, it is advisable running a test simulation to know the outputs that each simulation will have. We can do that with the method `get_outputs_df_from_testsim()`, which returns a tuple containing 2 DataFrames containing respectively the Output:Meter and Output:Variable objects from the simulation. In this case, you won't find wildcards such as \"*\"." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ff6093e9-840d-4142-ad79-98d78f4f13d1", + "metadata": {}, + "outputs": [], + "source": [ + "df_output_meters_testsim, df_output_variables_testsim = parametric.get_outputs_df_from_testsim()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "61a88134-7997-42ce-88b0-ef4606cdfe61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
0Heating:ElectricityHourly
1Cooling:ElectricityHourly
2Electricity:HVACHourly
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key_valuevariable_namefrequency
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly
2BLOCK1:ZONE2Zone Operative TemperatureHourly
3BLOCK1:ZONE1Zone Operative TemperatureHourly
4PEOPLE BLOCK1:ZONE2Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "2 BLOCK1:ZONE2 Zone Operative Temperature \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "4 PEOPLE BLOCK1:ZONE2 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency \n", + "0 Hourly \n", + "1 Hourly \n", + "2 Hourly \n", + "3 Hourly \n", + "4 Hourly \n", + "5 Hourly " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_testsim" + ] + }, + { + "cell_type": "markdown", + "id": "b0ed4500-6054-4a00-ae63-54412bf70944", + "metadata": {}, + "source": [ + "We can get DataFrames from the .rdd and .mdd files generated from the test simulation using the functions `get_rdd_file_as_df()` and `get_mdd_file_as_df()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e28454a5-8de7-4551-bf9b-4db3fa8f124f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectkey_valuevariable_namefrequencyunits
0Output:Variable*Site Outdoor Air Drybulb Temperaturehourly!- Zone Average [C]
1Output:Variable*Site Outdoor Air Dewpoint Temperaturehourly!- Zone Average [C]
2Output:Variable*Site Outdoor Air Wetbulb Temperaturehourly!- Zone Average [C]
3Output:Variable*Site Outdoor Air Humidity Ratiohourly!- Zone Average [kgWater/kgDryAir]
4Output:Variable*Site Outdoor Air Relative Humidityhourly!- Zone Average [%]
..................
712Output:Variable*Zone Ventilation When Unoccupied Timehourly!- HVAC Sum [hr]
713Output:Variable*Facility Any Zone Ventilation Below Target Voz...hourly!- HVAC Sum [hr]
714Output:Variable*Facility All Zones Ventilation At Target Voz Timehourly!- HVAC Sum [hr]
715Output:Variable*Facility Any Zone Ventilation Above Target Voz...hourly!- HVAC Sum [hr]
716Output:Variable*Facility Any Zone Ventilation When Unoccupied ...hourly!- HVAC Sum [hr]
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717 rows × 5 columns

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" + ], + "text/plain": [ + " object key_value \\\n", + "0 Output:Variable * \n", + "1 Output:Variable * \n", + "2 Output:Variable * \n", + "3 Output:Variable * \n", + "4 Output:Variable * \n", + ".. ... ... \n", + "712 Output:Variable * \n", + "713 Output:Variable * \n", + "714 Output:Variable * \n", + "715 Output:Variable * \n", + "716 Output:Variable * \n", + "\n", + " variable_name frequency \\\n", + "0 Site Outdoor Air Drybulb Temperature hourly \n", + "1 Site Outdoor Air Dewpoint Temperature hourly \n", + "2 Site Outdoor Air Wetbulb Temperature hourly \n", + "3 Site Outdoor Air Humidity Ratio hourly \n", + "4 Site Outdoor Air Relative Humidity hourly \n", + ".. ... ... \n", + "712 Zone Ventilation When Unoccupied Time hourly \n", + "713 Facility Any Zone Ventilation Below Target Voz... hourly \n", + "714 Facility All Zones Ventilation At Target Voz Time hourly \n", + "715 Facility Any Zone Ventilation Above Target Voz... hourly \n", + "716 Facility Any Zone Ventilation When Unoccupied ... hourly \n", + "\n", + " units \n", + "0 !- Zone Average [C] \n", + "1 !- Zone Average [C] \n", + "2 !- Zone Average [C] \n", + "3 !- Zone Average [kgWater/kgDryAir] \n", + "4 !- Zone Average [%] \n", + ".. ... \n", + "712 !- HVAC Sum [hr] \n", + "713 !- HVAC Sum [hr] \n", + "714 !- HVAC Sum [hr] \n", + "715 !- HVAC Sum [hr] \n", + "716 !- HVAC Sum [hr] \n", + "\n", + "[717 rows x 5 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rdd = get_rdd_file_as_df()\n", + "df_rdd" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "58649f6e-f08c-49e5-823a-376ff26eb83a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectmeter_namefrequencyunits
0Output:MeterElectricity:Facilityhourly!- [J]
1Output:Meter:CumulativeElectricity:Facilityhourly!- [J]
2Output:MeterElectricity:Buildinghourly!- [J]
3Output:Meter:CumulativeElectricity:Buildinghourly!- [J]
4Output:MeterElectricity:Zone:BLOCK1:ZONE2hourly!- [J]
...............
157Output:Meter:CumulativeGeneral:HeatRecovery:EnergyTransferhourly!- [J]
158Output:MeterCarbon Equivalent:Facilityhourly!- [kg]
159Output:Meter:CumulativeCarbon Equivalent:Facilityhourly!- [kg]
160Output:MeterCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
161Output:Meter:CumulativeCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
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162 rows × 4 columns

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" + ], + "text/plain": [ + " object meter_name \\\n", + "0 Output:Meter Electricity:Facility \n", + "1 Output:Meter:Cumulative Electricity:Facility \n", + "2 Output:Meter Electricity:Building \n", + "3 Output:Meter:Cumulative Electricity:Building \n", + "4 Output:Meter Electricity:Zone:BLOCK1:ZONE2 \n", + ".. ... ... \n", + "157 Output:Meter:Cumulative General:HeatRecovery:EnergyTransfer \n", + "158 Output:Meter Carbon Equivalent:Facility \n", + "159 Output:Meter:Cumulative Carbon Equivalent:Facility \n", + "160 Output:Meter CarbonEquivalentEmissions:Carbon Equivalent \n", + "161 Output:Meter:Cumulative CarbonEquivalentEmissions:Carbon Equivalent \n", + "\n", + " frequency units \n", + "0 hourly !- [J] \n", + "1 hourly !- [J] \n", + "2 hourly !- [J] \n", + "3 hourly !- [J] \n", + "4 hourly !- [J] \n", + ".. ... ... \n", + "157 hourly !- [J] \n", + "158 hourly !- [kg] \n", + "159 hourly !- [kg] \n", + "160 hourly !- [kg] \n", + "161 hourly !- [kg] \n", + "\n", + "[162 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mdd = get_mdd_file_as_df()\n", + "df_mdd" + ] + }, + { + "cell_type": "markdown", + "id": "a84943b5-db09-4f19-95bb-f9583711d0a7", + "metadata": {}, + "source": [ + "Also, we can parse the .mtd files as a list using the function `parse_mtd_file()`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df4da1a7-fcb2-4c59-b790-1bc9e3b128f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'meter_id': '14',\n", + " 'description': 'BLOCK1:ZONE2 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']},\n", + " {'meter_id': '135',\n", + " 'description': 'BLOCK1:ZONE1 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']}]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mtd_list = parse_mtd_file()\n", + "mtd_list[0:2]" + ] + }, + { + "cell_type": "markdown", + "id": "76fa398d-15a0-4504-9f11-468bfd7c094f", + "metadata": {}, + "source": [ + "Therefore, we have 2 DataFrames, one for the Output:Meter and another for the Output:Variable objects. Next step is setting the outputs for the parametric simulation. To do so, we'll need to pass the DataFrames into the method `set_outputs_for_simulation(df_output_meter, df_output_variable)`. If you have some knowledge about the python package besos, you might think of these dataframes as if each row was a `MeterReader` or `VariableReader` instances respectively for the Output:Meter and Output:Variable dataframes, and the arguments in these were the specified in the columns. The `MeterReader` class takes the arguments `key_name`, `frequency`, `name` and `func`, while `VariableReader` class takes the arguments `key_value`, `variable_name`, `frequency`, `name` and `func`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b133883-a0be-42a9-bc04-0e6054337b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_name', 'frequency']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_meters_testsim.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c43b0496-1d36-4a0e-b2dc-9f77e41627ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_value', 'variable_name', 'frequency']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_variables_testsim.columns]" + ] + }, + { + "cell_type": "markdown", + "id": "a03bd526-8bdf-4cce-af62-73ac36e9d0c3", + "metadata": {}, + "source": [ + "If you take a look at the columns of the dataframes above, you can see the names are the arguments in the `MeterReader` and `VariableReader` classes, and only `name` and `func` are missing. That means, you can add these columns to input the `name` and `func` arguments as desired. In case of the Output:Meter dataframe, we won't add the `name` and `func` columns, which means the name will be the `key_name` and hourly results will be aggregated using the pd.Series.sum() function. However, in case of the Output:Variable dataframe, we will specify these: we want the hourly values rather than the aggregation, therefore we will pass the name bound to the function `return_time_series`, and we will add '_time series' as a suffix to the `variable_name` column. We will also remove the outputs for BLOCK1:ZONE2, which are the rows 2 and 4." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0e8fecdc-4625-4d95-a87d-67071884963b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namefrequencyfuncname
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x0000013FBD78...Adaptive Cooling Setpoint Temperature_No Toler...
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x0000013FBD78...Adaptive Heating Setpoint Temperature_No Toler...
3BLOCK1:ZONE1Zone Operative TemperatureHourly<function return_time_series at 0x0000013FBD78...Zone Operative Temperature_time series
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly<function return_time_series at 0x0000013FBD78...Zone Thermal Comfort ASHRAE 55 Adaptive Model ...
\n", + "
" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency func \\\n", + "0 Hourly , key_name='Heating:Electricity'),\n", + " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", + " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", + " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", + " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.sim_outputs" + ] + }, + { + "cell_type": "markdown", + "id": "b5dd98bb-6b1d-4b3c-b762-3c9a0c015f63", + "metadata": {}, + "source": [ + "## Setting the parameters" + ] + }, + { + "cell_type": "markdown", + "id": "4804beed-52e5-432a-a999-685c222d668d", + "metadata": {}, + "source": [ + "At the top of the script, when you instantiated the class `OptimParamSimulation`, you already specified which type of parameters you were going to use. Now, the parameters we're about to set, must match the `parameters_type` argument. At this point, you may not know which parameters you can use, so you can call the method `get_available_parameters()`, which will return a list of available parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "004029fa-ff72-4f43-a9c3-e004757463e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CustAST_ACSTaul',\n", + " 'CustAST_ACSTall',\n", + " 'CustAST_AHSTaul',\n", + " 'CustAST_AHSTall',\n", + " 'CustAST_ASTaul',\n", + " 'CustAST_ASTall',\n", + " 'CustAST_m',\n", + " 'CustAST_n',\n", + " 'CustAST_ACSToffset',\n", + " 'CustAST_AHSToffset',\n", + " 'CustAST_ASToffset']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "available_parameters = parametric.get_available_parameters()\n", + "available_parameters" + ] + }, + { + "cell_type": "markdown", + "id": "45886126-30b8-4e53-b8c0-06135e6a3c38", + "metadata": {}, + "source": [ + "If you don't know what are these, please refer to the [documentation](https://accim.readthedocs.io/en/master/4_detailed%20use.html).\n", + "\n", + "Using the 'accim custom model' type, the values can be either a list of options or a range of values. Now, let's set the parameters using the method `set_parameters(accis_params_dict, additional_params)`. In this method, we set the parameters related to accim using the argument `accis_params_dict`, which takes a dictionary following the pattern {'parameter name': [1, 2, 3, etc]} in case of list of options, or {'parameter name': (min_value, max_value)} in case of the range of values. We can also add some other parameters not related to accim in the argument `additional_params`, which takes a list of parameters as if these were input straight to the besos EPProblem class." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b4eb43dd-879b-4175-ab56-e9c04ea342af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_m.The name used as an input (CustAST_m) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_n.The name used as an input (CustAST_n) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_ASToffset.The name used as an input (CustAST_ASToffset) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_ASTall.The name used as an input (CustAST_ASTall) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_ASTaul.The name used as an input (CustAST_ASTaul) will be discarded.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "accis_parameters = {\n", + " 'CustAST_m': (0.01, 0.99),\n", + " 'CustAST_n': (5, 23),\n", + " 'CustAST_ASToffset': (2, 4),\n", + " 'CustAST_ASTall': (10, 15),\n", + " 'CustAST_ASTaul': (30, 35),\n", + "}\n", + "\n", + "parametric.set_parameters(accis_params_dict=accis_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "077a85a9-4493-4f7f-b3f6-e305dc9eb136", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetInputData, !- Name\n", + " set ComfStand = 99, !- Program Line 1\n", + " set CAT = 80, !- Program Line 2\n", + " set ComfMod = 3, !- Program Line 3\n", + " set HVACmode = 2, !- Program Line 4\n", + " set VentCtrl = 0, !- Program Line 5\n", + " set VSToffset = 0, !- Program Line 6\n", + " set MinOToffset = 50, !- Program Line 7\n", + " set MaxWindSpeed = 50, !- Program Line 8\n", + " set ACSTtol = -0.1, !- Program Line 9\n", + " set AHSTtol = 0.1, !- Program Line 10\n", + " set CoolSeasonStart = 121, !- Program Line 11\n", + " set CoolSeasonEnd = 274; !- Program Line 12]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setinputdata']" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "af64d963-4114-4bde-8c74-70f8b825fbe9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " ApplyCAT, !- Name\n", + " set CATcoolOffset = 0, !- Program Line 1\n", + " set CATheatOffset = 0, !- Program Line 2\n", + " if ComfStand == 99, !- Program Line 3\n", + " set ACSToffset = 0 + 0, !- Program Line 4\n", + " set AHSToffset = 0 + 0, !- Program Line 5\n", + " elseif (ComfStand == 1 ), !- Program Line 6\n", + " if (CAT == 1), !- Program Line 7\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 8\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 9\n", + " elseif (CAT == 2), !- Program Line 10\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 11\n", + " set AHSToffset = -4+CATheatOffset, !- Program Line 12\n", + " elseif (CAT == 3), !- Program Line 13\n", + " set ACSToffset = 4+CATcoolOffset, !- Program Line 14\n", + " set AHSToffset = -5+CATheatOffset, !- Program Line 15\n", + " endif, !- Program Line 16\n", + " elseif ComfStand == 2 || ComfStand == 3 || ComfStand == 11, !- Program Line 17\n", + " if (CAT == 90), !- Program Line 18\n", + " set ACSToffset = 2.5+CATcoolOffset, !- Program Line 19\n", + " set AHSToffset = -2.5+CATheatOffset, !- Program Line 20\n", + " elseif (CAT == 80), !- Program Line 21\n", + " set ACSToffset = 3.5+CATcoolOffset, !- Program Line 22\n", + " set AHSToffset = -3.5+CATheatOffset, !- Program Line 23\n", + " endif, !- Program Line 24\n", + " elseif (ComfStand == 4 ) || (ComfStand == 5) || (ComfStand == 6), !- Program Line 25\n", + " set ACSToffset = 0+CATcoolOffset, !- Program Line 26\n", + " set AHSToffset = 0+CATheatOffset, !- Program Line 27\n", + " elseif (ComfStand == 7), !- Program Line 28\n", + " if (CAT == 90), !- Program Line 29\n", + " set ACSToffset = 2.4+CATcoolOffset, !- Program Line 30\n", + " set AHSToffset = -2.4+CATheatOffset, !- Program Line 31\n", + " elseif (CAT == 85), !- Program Line 32\n", + " set ACSToffset = 3.3+CATcoolOffset, !- Program Line 33\n", + " set AHSToffset = -3.3+CATheatOffset, !- Program Line 34\n", + " elseif (CAT == 80), !- Program Line 35\n", + " set ACSToffset = 4.1+CATcoolOffset, !- Program Line 36\n", + " set AHSToffset = -4.1+CATheatOffset, !- Program Line 37\n", + " endif, !- Program Line 38\n", + " elseif (ComfStand == 8), !- Program Line 39\n", + " if (CAT == 90), !- Program Line 40\n", + " set ACSToffset = 3.5+CATcoolOffset, !- Program Line 41\n", + " set AHSToffset = -3.5+CATheatOffset, !- Program Line 42\n", + " elseif (CAT == 85), !- Program Line 43\n", + " set ACSToffset = 4.8+CATcoolOffset, !- Program Line 44\n", + " set AHSToffset = -4.8+CATheatOffset, !- Program Line 45\n", + " elseif (CAT == 80), !- Program Line 46\n", + " set ACSToffset = 5.9+CATcoolOffset, !- Program Line 47\n", + " set AHSToffset = -5.9+CATheatOffset, !- Program Line 48\n", + " endif, !- Program Line 49\n", + " elseif ComfStand == 9 || ComfStand == 10, !- Program Line 50\n", + " if (CAT == 90), !- Program Line 51\n", + " set ACSToffset = 2.15+CATcoolOffset, !- Program Line 52\n", + " set AHSToffset = -2.15+CATheatOffset, !- Program Line 53\n", + " elseif (CAT == 80), !- Program Line 54\n", + " set ACSToffset = 3.6+CATcoolOffset, !- Program Line 55\n", + " set AHSToffset = -3.6+CATheatOffset, !- Program Line 56\n", + " endif, !- Program Line 57\n", + " elseif ComfStand == 12, !- Program Line 58\n", + " if (CAT == 90), !- Program Line 59\n", + " set ACSToffset = 1.7+CATcoolOffset, !- Program Line 60\n", + " set AHSToffset = -1.7+CATheatOffset, !- Program Line 61\n", + " elseif (CAT == 80), !- Program Line 62\n", + " set ACSToffset = 2.89+CATcoolOffset, !- Program Line 63\n", + " set AHSToffset = -2.89+CATheatOffset, !- Program Line 64\n", + " endif, !- Program Line 65\n", + " elseif ComfStand == 13, !- Program Line 66\n", + " if (CAT == 90), !- Program Line 67\n", + " set ACSToffset = 3.45+CATcoolOffset, !- Program Line 68\n", + " set AHSToffset = -3.45+CATheatOffset, !- Program Line 69\n", + " elseif (CAT == 80), !- Program Line 70\n", + " set ACSToffset = 4.55+CATcoolOffset, !- Program Line 71\n", + " set AHSToffset = -4.55+CATheatOffset, !- Program Line 72\n", + " endif, !- Program Line 73\n", + " elseif ComfStand == 14, !- Program Line 74\n", + " if (CAT == 90), !- Program Line 75\n", + " set ACSToffset = 3.5+CATcoolOffset, !- Program Line 76\n", + " set AHSToffset = -3.5+CATheatOffset, !- Program Line 77\n", + " elseif (CAT == 80), !- Program Line 78\n", + " set ACSToffset = 4.5+CATcoolOffset, !- Program Line 79\n", + " set AHSToffset = -4.5+CATheatOffset, !- Program Line 80\n", + " endif, !- Program Line 81\n", + " elseif ComfStand == 15, !- Program Line 82\n", + " if (CAT == 90), !- Program Line 83\n", + " set ACSToffset = 2.8+CATcoolOffset, !- Program Line 84\n", + " set AHSToffset = -2.8+CATheatOffset, !- Program Line 85\n", + " elseif (CAT == 80), !- Program Line 86\n", + " set ACSToffset = 3.8+CATcoolOffset, !- Program Line 87\n", + " set AHSToffset = -3.8+CATheatOffset, !- Program Line 88\n", + " endif, !- Program Line 89\n", + " elseif ComfStand == 16, !- Program Line 90\n", + " if (CAT == 90), !- Program Line 91\n", + " set ACSToffset = 1.1+CATcoolOffset, !- Program Line 92\n", + " set AHSToffset = -1.1+CATheatOffset, !- Program Line 93\n", + " elseif (CAT == 80), !- Program Line 94\n", + " set ACSToffset = 2.1+CATcoolOffset, !- Program Line 95\n", + " set AHSToffset = -2.1+CATheatOffset, !- Program Line 96\n", + " endif, !- Program Line 97\n", + " elseif (ComfStand == 17) || (ComfStand == 18), !- Program Line 98\n", + " if CAT == 90, !- Program Line 99\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 100\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 101\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 102\n", + " else, !- Program Line 103\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 104\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 105\n", + " endif, !- Program Line 106\n", + " elseif CAT == 80, !- Program Line 107\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 108\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 109\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 110\n", + " else, !- Program Line 111\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 112\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 113\n", + " endif, !- Program Line 114\n", + " endif, !- Program Line 115\n", + " elseif ComfStand == 19, !- Program Line 116\n", + " if CAT == 90, !- Program Line 117\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 118\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 119\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 120\n", + " else, !- Program Line 121\n", + " set ACSToffset = 1+CATcoolOffset, !- Program Line 122\n", + " set AHSToffset = -1+CATheatOffset, !- Program Line 123\n", + " endif, !- Program Line 124\n", + " elseif CAT == 80, !- Program Line 125\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 126\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 127\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 128\n", + " else, !- Program Line 129\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 130\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 131\n", + " endif, !- Program Line 132\n", + " endif, !- Program Line 133\n", + " elseif ComfStand == 20, !- Program Line 134\n", + " if CAT == 90, !- Program Line 135\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 136\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 137\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 138\n", + " else, !- Program Line 139\n", + " set ACSToffset = 5+CATcoolOffset, !- Program Line 140\n", + " set AHSToffset = -5+CATheatOffset, !- Program Line 141\n", + " endif, !- Program Line 142\n", + " elseif CAT == 80, !- Program Line 143\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 144\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 145\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 146\n", + " else, !- Program Line 147\n", + " set ACSToffset = 6+CATcoolOffset, !- Program Line 148\n", + " set AHSToffset = -6+CATheatOffset, !- Program Line 149\n", + " endif, !- Program Line 150\n", + " endif, !- Program Line 151\n", + " elseif ComfStand == 21, !- Program Line 152\n", + " if (CAT == 90), !- Program Line 153\n", + " set ACSToffset = 2.5+CATcoolOffset, !- Program Line 154\n", + " set AHSToffset = -2.5+CATheatOffset, !- Program Line 155\n", + " elseif (CAT == 80), !- Program Line 156\n", + " set ACSToffset = 4+CATcoolOffset, !- Program Line 157\n", + " set AHSToffset = -4+CATheatOffset, !- Program Line 158\n", + " endif, !- Program Line 159\n", + " endif; !- Program Line 160]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'applycat']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "35cfc985-91fd-4b66-ad7e-076319dbe5e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetAST, !- Name\n", + " set SetpointAcc = 10000, !- Program Line 1\n", + " set m = 0, !- Program Line 2\n", + " set n = 0, !- Program Line 3\n", + " if CoolSeasonEnd > CoolSeasonStart, !- Program Line 4\n", + " if (DayOfYear >= CoolSeasonStart) && (DayOfYear < CoolSeasonEnd), !- Program Line 5\n", + " set CoolingSeason = 1, !- Program Line 6\n", + " else, !- Program Line 7\n", + " set CoolingSeason = 0, !- Program Line 8\n", + " endif, !- Program Line 9\n", + " elseif CoolSeasonStart > CoolSeasonEnd, !- Program Line 10\n", + " if (DayOfYear >= CoolSeasonStart) || (DayOfYear < CoolSeasonEnd), !- Program Line 11\n", + " set CoolingSeason = 1, !- Program Line 12\n", + " else, !- Program Line 13\n", + " set CoolingSeason = 0, !- Program Line 14\n", + " endif, !- Program Line 15\n", + " endif, !- Program Line 16\n", + " if (ComfStand == 99) && (ComfMod == 3), !- Program Line 17\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 18\n", + " set ACST = PMOT*m+n+ACSToffset+ACSTtol, !- Program Line 19\n", + " elseif PMOT < ACSTall, !- Program Line 20\n", + " set ACST = ACSTall*m+n+ACSToffset+ACSTtol, !- Program Line 21\n", + " elseif PMOT > ACSTaul, !- Program Line 22\n", + " set ACST = ACSTaul*m+n+ACSToffset+ACSTtol, !- Program Line 23\n", + " endif, !- Program Line 24\n", + " endif, !- Program Line 25\n", + " if (ComfStand == 99) && (ComfMod == 3), !- Program Line 26\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 27\n", + " set AHST = PMOT*m+n+AHSToffset+AHSTtol, !- Program Line 28\n", + " elseif PMOT < AHSTall, !- Program Line 29\n", + " set AHST = AHSTall*m+n+AHSToffset+AHSTtol, !- Program Line 30\n", + " elseif PMOT > AHSTaul, !- Program Line 31\n", + " set AHST = AHSTaul*m+n+AHSToffset+AHSTtol, !- Program Line 32\n", + " endif, !- Program Line 33\n", + " endif, !- Program Line 34\n", + " if (ComfStand == 0) && (CurrentTime < 8), !- Program Line 35\n", + " set ACST = 27+ACSTtol, !- Program Line 36\n", + " set AHST = 17+AHSTtol, !- Program Line 37\n", + " elseif (ComfStand == 0) && (CurrentTime < 16), !- Program Line 38\n", + " set ACST = 25+ACSTtol, !- Program Line 39\n", + " set AHST = 20+AHSTtol, !- Program Line 40\n", + " elseif (ComfStand == 0) && (CurrentTime < 23), !- Program Line 41\n", + " set ACST = 25+ACSTtol, !- Program Line 42\n", + " set AHST = 20+AHSTtol, !- Program Line 43\n", + " elseif (ComfStand == 0) && (CurrentTime < 24), !- Program Line 44\n", + " set ACST = 27+ACSTtol, !- Program Line 45\n", + " set AHST = 17+AHSTtol, !- Program Line 46\n", + " endif, !- Program Line 47\n", + " if (ComfStand == 1) && (ComfMod == 0), !- Program Line 48\n", + " if CoolingSeason == 1, !- Program Line 49\n", + " if (CAT==1), !- Program Line 50\n", + " set ACST = 25.5+ACSTtol, !- Program Line 51\n", + " elseif (CAT==2), !- Program Line 52\n", + " set ACST = 26+ACSTtol, !- Program Line 53\n", + " elseif (CAT==3), !- Program Line 54\n", + " set ACST = 27+ACSTtol, !- Program Line 55\n", + " endif, !- Program Line 56\n", + " else, !- Program Line 57\n", + " if (CAT==1), !- Program Line 58\n", + " set ACST = 25+ACSTtol, !- Program Line 59\n", + " elseif (CAT==2), !- Program Line 60\n", + " set ACST = 25+ACSTtol, !- Program Line 61\n", + " elseif (CAT==3), !- Program Line 62\n", + " set ACST = 25+ACSTtol, !- Program Line 63\n", + " endif, !- Program Line 64\n", + " endif, !- Program Line 65\n", + " endif, !- Program Line 66\n", + " if (ComfStand == 1) && (ComfMod == 0), !- Program Line 67\n", + " if CoolingSeason == 1, !- Program Line 68\n", + " if (CAT==1), !- Program Line 69\n", + " set AHST = 23.5+AHSTtol, !- Program Line 70\n", + " elseif (CAT==2), !- Program Line 71\n", + " set AHST = 23+AHSTtol, !- Program Line 72\n", + " elseif (CAT==3), !- Program Line 73\n", + " set AHST = 22+AHSTtol, !- Program Line 74\n", + " endif, !- Program Line 75\n", + " else, !- Program Line 76\n", + " if (CAT==1), !- Program Line 77\n", + " set AHST = 21+AHSTtol, !- Program Line 78\n", + " elseif (CAT==2), !- Program Line 79\n", + " set AHST = 20+AHSTtol, !- Program Line 80\n", + " elseif (CAT==3), !- Program Line 81\n", + " set AHST = 18+AHSTtol, !- Program Line 82\n", + " endif, !- Program Line 83\n", + " endif, !- Program Line 84\n", + " endif, !- Program Line 85\n", + " if (ComfStand == 1) && (ComfMod == 1), !- Program Line 86\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 87\n", + " set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 88\n", + " elseif CurrentTime < 7, !- Program Line 89\n", + " set ACST = 27+ACSTtol, !- Program Line 90\n", + " elseif CurrentTime < 15, !- Program Line 91\n", + " set ACST = 50, !- Program Line 92\n", + " elseif CurrentTime < 23, !- Program Line 93\n", + " set ACST = 25+ACSTtol, !- Program Line 94\n", + " elseif CurrentTime < 24, !- Program Line 95\n", + " set ACST = 27+ACSTtol, !- Program Line 96\n", + " endif, !- Program Line 97\n", + " endif, !- Program Line 98\n", + " if (ComfStand == 1) && (ComfMod == 1), !- Program Line 99\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 100\n", + " set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 101\n", + " elseif CurrentTime < 7, !- Program Line 102\n", + " set AHST = 17+AHSTtol, !- Program Line 103\n", + " elseif CurrentTime < 23, !- Program Line 104\n", + " set AHST = 20+AHSTtol, !- Program Line 105\n", + " elseif CurrentTime < 24, !- Program Line 106\n", + " set AHST = 17+AHSTtol, !- Program Line 107\n", + " endif, !- Program Line 108\n", + " endif, !- Program Line 109\n", + " if (ComfStand == 1) && (ComfMod == 2), !- Program Line 110\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 111\n", + " set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 112\n", + " elseif (RMOT < ACSTall) && (CAT==1), !- Program Line 113\n", + " set ACST = 25+ACSTtol, !- Program Line 114\n", + " elseif (RMOT > ACSTaul) && (CAT==1), !- Program Line 115\n", + " set ACST = 25.5+ACSTtol, !- Program Line 116\n", + " elseif (RMOT < ACSTall) && (CAT==2), !- Program Line 117\n", + " set ACST = 25+ACSTtol, !- Program Line 118\n", + " elseif (RMOT > ACSTaul) && (CAT==2), !- Program Line 119\n", + " set ACST = 26+ACSTtol, !- Program Line 120\n", + " elseif (RMOT < ACSTall) && (CAT==3), !- Program Line 121\n", + " set ACST = 25+ACSTtol, !- Program Line 122\n", + " elseif (RMOT > ACSTaul) && (CAT==3), !- Program Line 123\n", + " set ACST = 27+ACSTtol, !- Program Line 124\n", + " endif, !- Program Line 125\n", + " endif, !- Program Line 126\n", + " if (ComfStand == 1) && (ComfMod == 2), !- Program Line 127\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 128\n", + " set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 129\n", + " elseif (RMOT < AHSTall) && (CAT==1), !- Program Line 130\n", + " set AHST = 21+AHSTtol, !- Program Line 131\n", + " elseif (RMOT > AHSTaul) && (CAT==1), !- Program Line 132\n", + " set AHST = 23.5+AHSTtol, !- Program Line 133\n", + " elseif (RMOT < AHSTall) && (CAT==2), !- Program Line 134\n", + " set AHST = 20+AHSTtol, !- Program Line 135\n", + " elseif (RMOT > AHSTaul) && (CAT==2), !- Program Line 136\n", + " set AHST = 23+AHSTtol, !- Program Line 137\n", + " elseif (RMOT < AHSTall) && (CAT==3), !- Program Line 138\n", + " set AHST = 18+AHSTtol, !- Program Line 139\n", + " elseif (RMOT > AHSTaul) && (CAT==3), !- Program Line 140\n", + " set AHST = 22+AHSTtol, !- Program Line 141\n", + " endif, !- Program Line 142\n", + " endif, !- Program Line 143\n", + " if (ComfStand == 1) && (ComfMod == 3), !- Program Line 144\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 145\n", + " set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 146\n", + " elseif RMOT < ACSTall, !- Program Line 147\n", + " set ACST = ACSTall*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 148\n", + " elseif RMOT > ACSTaul, !- Program Line 149\n", + " set ACST = ACSTaul*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 150\n", + " endif, !- Program Line 151\n", + " endif, !- Program Line 152\n", + " if (ComfStand == 1) && (ComfMod == 3), !- Program Line 153\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 154\n", + " set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 155\n", + " elseif RMOT < AHSTall, !- Program Line 156\n", + " set AHST = AHSTall*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 157\n", + " elseif RMOT > AHSTaul, !- Program Line 158\n", + " set AHST = AHSTaul*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 159\n", + " endif, !- Program Line 160\n", + " endif, !- Program Line 161\n", + " if (ComfStand == 2) && (ComfMod == 0), !- Program Line 162\n", + " if CoolingSeason == 1, !- Program Line 163\n", + " if (CAT==80), !- Program Line 164\n", + " set ACST = 27+ACSTtol, !- Program Line 165\n", + " elseif (CAT==90), !- Program Line 166\n", + " set ACST = 26+ACSTtol, !- Program Line 167\n", + " endif, !- Program Line 168\n", + " else, !- Program Line 169\n", + " if (CAT==80), !- Program Line 170\n", + " set ACST = 25+ACSTtol, !- Program Line 171\n", + " elseif (CAT==90), !- Program Line 172\n", + " set ACST = 24+ACSTtol, !- Program Line 173\n", + " endif, !- Program Line 174\n", + " endif, !- Program Line 175\n", + " endif, !- Program Line 176\n", + " if (ComfStand == 2) && (ComfMod == 0), !- Program Line 177\n", + " if CoolingSeason == 1, !- Program Line 178\n", + " if (CAT==80), !- Program Line 179\n", + " set AHST = 22+AHSTtol, !- Program Line 180\n", + " elseif (CAT==90), !- Program Line 181\n", + " set AHST = 23+AHSTtol, !- Program Line 182\n", + " endif, !- Program Line 183\n", + " else, !- Program Line 184\n", + " if (CAT==80), !- Program Line 185\n", + " set AHST = 19+AHSTtol, !- Program Line 186\n", + " elseif (CAT==90), !- Program Line 187\n", + " set AHST = 20+AHSTtol, !- Program Line 188\n", + " endif, !- Program Line 189\n", + " endif, !- Program Line 190\n", + " endif, !- Program Line 191\n", + " if (ComfStand == 2) && (ComfMod == 1), !- Program Line 192\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 193\n", + " set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 194\n", + " elseif CurrentTime < 7, !- Program Line 195\n", + " set ACST = 27+ACSTtol, !- Program Line 196\n", + " elseif CurrentTime < 15, !- Program Line 197\n", + " set ACST = 50, !- Program Line 198\n", + " elseif CurrentTime < 23, !- Program Line 199\n", + " set ACST = 25+ACSTtol, !- Program Line 200\n", + " elseif CurrentTime < 24, !- Program Line 201\n", + " set ACST = 27+ACSTtol, !- Program Line 202\n", + " endif, !- Program Line 203\n", + " endif, !- Program Line 204\n", + " if (ComfStand == 2) && (ComfMod == 1), !- Program Line 205\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 206\n", + " set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 207\n", + " elseif CurrentTime < 7, !- Program Line 208\n", + " set AHST = 17+AHSTtol, !- Program Line 209\n", + " elseif CurrentTime < 23, !- Program Line 210\n", + " set AHST = 20+AHSTtol, !- Program Line 211\n", + " elseif CurrentTime < 24, !- Program Line 212\n", + " set AHST = 17+AHSTtol, !- Program Line 213\n", + " endif, !- Program Line 214\n", + " endif, !- Program Line 215\n", + " if (ComfStand == 2) && (ComfMod == 2), !- Program Line 216\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 217\n", + " set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 218\n", + " elseif CAT==80, !- Program Line 219\n", + " if PMOT < ACSTall, !- Program Line 220\n", + " set ACST = 25+ACSTtol, !- Program Line 221\n", + " elseif PMOT > ACSTaul, !- Program Line 222\n", + " set ACST = 27+ACSTtol, !- Program Line 223\n", + " endif, !- Program Line 224\n", + " elseif CAT==90, !- Program Line 225\n", + " if PMOT < ACSTall, !- Program Line 226\n", + " set ACST = 24+ACSTtol, !- Program Line 227\n", + " elseif PMOT > ACSTaul, !- Program Line 228\n", + " set ACST = 26+ACSTtol, !- Program Line 229\n", + " endif, !- Program Line 230\n", + " endif, !- Program Line 231\n", + " endif, !- Program Line 232\n", + " if (ComfStand == 2) && (ComfMod == 2), !- Program Line 233\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 234\n", + " set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 235\n", + " elseif CAT==80, !- Program Line 236\n", + " if PMOT < AHSTall, !- Program Line 237\n", + " set AHST = 19+AHSTtol, !- Program Line 238\n", + " elseif PMOT > AHSTaul, !- Program Line 239\n", + " set AHST = 22+AHSTtol, !- Program Line 240\n", + " endif, !- Program Line 241\n", + " elseif CAT==90, !- Program Line 242\n", + " if PMOT < AHSTall, !- Program Line 243\n", + " set AHST = 20+AHSTtol, !- Program Line 244\n", + " elseif PMOT > AHSTaul, !- Program Line 245\n", + " set AHST = 23+AHSTtol, !- Program Line 246\n", + " endif, !- Program Line 247\n", + " endif, !- Program Line 248\n", + " endif, !- Program Line 249\n", + " if (ComfStand == 2) && (ComfMod == 3), !- Program Line 250\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 251\n", + " set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 252\n", + " elseif PMOT < ACSTall, !- Program Line 253\n", + " set ACST = ACSTall*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 254\n", + " elseif PMOT > ACSTaul, !- Program Line 255\n", + " set ACST = ACSTaul*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 256\n", + " endif, !- Program Line 257\n", + " endif, !- Program Line 258\n", + " if (ComfStand == 2) && (ComfMod == 3), !- Program Line 259\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 260\n", + " set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 261\n", + " elseif PMOT < AHSTall, !- Program Line 262\n", + " set AHST = AHSTall*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 263\n", + " elseif PMOT > AHSTaul, !- Program Line 264\n", + " set AHST = AHSTaul*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 265\n", + " endif, !- Program Line 266\n", + " endif, !- Program Line 267\n", + " if (ComfStand == 3) && (ComfMod == 0), !- Program Line 268\n", + " if (CAT==80), !- Program Line 269\n", + " set ACST = 28+ACSTtol, !- Program Line 270\n", + " elseif (CAT==90), !- Program Line 271\n", + " set ACST = 27+ACSTtol, !- Program Line 272\n", + " endif, !- Program Line 273\n", + " endif, !- Program Line 274\n", + " if (ComfStand == 3) && (ComfMod == 0), !- Program Line 275\n", + " if (CAT==80), !- Program Line 276\n", + " set AHST = 18+AHSTtol, !- Program Line 277\n", + " elseif (CAT==90), !- Program Line 278\n", + " set AHST = 19+AHSTtol, !- Program Line 279\n", + " endif, !- Program Line 280\n", + " endif, !- Program Line 281\n", + " if (ComfStand == 3) && (ComfMod == 1), !- Program Line 282\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 283\n", + " set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 284\n", + " elseif CAT==80, !- Program Line 285\n", + " if PMOT < ACSTall, !- Program Line 286\n", + " set ACST = 28+ACSTtol, !- Program Line 287\n", + " elseif PMOT > ACSTaul, !- Program Line 288\n", + " set ACST = 28+ACSTtol, !- Program Line 289\n", + " endif, !- Program Line 290\n", + " elseif CAT==90, !- Program Line 291\n", + " if PMOT < ACSTall, !- Program Line 292\n", + " set ACST = 27+ACSTtol, !- Program Line 293\n", + " elseif PMOT > ACSTaul, !- Program Line 294\n", + " set ACST = 27+ACSTtol, !- Program Line 295\n", + " endif, !- Program Line 296\n", + " endif, !- Program Line 297\n", + " endif, !- Program Line 298\n", + " if (ComfStand == 3) && (ComfMod == 1), !- Program Line 299\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 300\n", + " set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 301\n", + " elseif CAT==80, !- Program Line 302\n", + " if PMOT < AHSTall, !- Program Line 303\n", + " set AHST = 18+AHSTtol, !- Program Line 304\n", + " elseif PMOT > AHSTaul, !- Program Line 305\n", + " set AHST = 18+AHSTtol, !- Program Line 306\n", + " endif, !- Program Line 307\n", + " elseif CAT==90, !- Program Line 308\n", + " if PMOT < AHSTall, !- Program Line 309\n", + " set AHST = 19+AHSTtol, !- Program Line 310\n", + " elseif PMOT > AHSTaul, !- Program Line 311\n", + " set AHST = 19+AHSTtol, !- Program Line 312\n", + " endif, !- Program Line 313\n", + " endif, !- Program Line 314\n", + " endif, !- Program Line 315\n", + " if (ComfStand == 3) && (ComfMod == 2), !- Program Line 316\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 317\n", + " set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 318\n", + " elseif CAT==80, !- Program Line 319\n", + " if PMOT < ACSTall, !- Program Line 320\n", + " set ACST = 25+ACSTtol, !- Program Line 321\n", + " elseif PMOT > ACSTaul, !- Program Line 322\n", + " set ACST = 27+ACSTtol, !- Program Line 323\n", + " endif, !- Program Line 324\n", + " elseif CAT==90, !- Program Line 325\n", + " if PMOT < ACSTall, !- Program Line 326\n", + " set ACST = 24+ACSTtol, !- Program Line 327\n", + " elseif PMOT > ACSTaul, !- Program Line 328\n", + " set ACST = 26+ACSTtol, !- Program Line 329\n", + " endif, !- Program Line 330\n", + " endif, !- Program Line 331\n", + " endif, !- Program Line 332\n", + " if (ComfStand == 3) && (ComfMod == 2), !- Program Line 333\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 334\n", + " set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 335\n", + " elseif CAT==80, !- Program Line 336\n", + " if PMOT < AHSTall, !- Program Line 337\n", + " set AHST = 19+AHSTtol, !- Program Line 338\n", + " elseif PMOT > AHSTaul, !- Program Line 339\n", + " set AHST = 22+AHSTtol, !- Program Line 340\n", + " endif, !- Program Line 341\n", + " elseif CAT==90, !- Program Line 342\n", + " if PMOT < AHSTall, !- Program Line 343\n", + " set AHST = 20+AHSTtol, !- Program Line 344\n", + " elseif PMOT > AHSTaul, !- Program Line 345\n", + " set AHST = 23+AHSTtol, !- Program Line 346\n", + " endif, !- Program Line 347\n", + " endif, !- Program Line 348\n", + " endif, !- Program Line 349\n", + " if (ComfStand == 3) && (ComfMod == 3), !- Program Line 350\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 351\n", + " set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 352\n", + " elseif PMOT < ACSTall, !- Program Line 353\n", + " set ACST = ACSTall*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 354\n", + " elseif PMOT > ACSTaul, !- Program Line 355\n", + " set ACST = ACSTaul*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 356\n", + " endif, !- Program Line 357\n", + " endif, !- Program Line 358\n", + " if (ComfStand == 3) && (ComfMod == 3), !- Program Line 359\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 360\n", + " set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 361\n", + " elseif PMOT < AHSTall, !- Program Line 362\n", + " set AHST = AHSTall*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 363\n", + " elseif PMOT > AHSTaul, !- Program Line 364\n", + " set AHST = AHSTaul*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 365\n", + " endif, !- Program Line 366\n", + " endif, !- Program Line 367\n", + " if (ComfStand == 4) && (ComfMod == 3), !- Program Line 368\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 369\n", + " if CAT == 1, !- Program Line 370\n", + " set ACST = PMOT*0.77+12.04+ACSTtol, !- Program Line 371\n", + " elseif CAT == 2, !- Program Line 372\n", + " set ACST = PMOT*0.73+15.28+ACSTtol, !- Program Line 373\n", + " endif, !- Program Line 374\n", + " elseif PMOT < ACSTall, !- Program Line 375\n", + " if CAT == 1, !- Program Line 376\n", + " set ACST = ACSTall*0.77+12.04+ACSTtol, !- Program Line 377\n", + " elseif CAT == 2, !- Program Line 378\n", + " set ACST = ACSTall*0.73+15.28+ACSTtol, !- Program Line 379\n", + " endif, !- Program Line 380\n", + " elseif PMOT > ACSTaul, !- Program Line 381\n", + " if CAT == 1, !- Program Line 382\n", + " set ACST = ACSTaul*0.77+12.04+ACSTtol, !- Program Line 383\n", + " elseif CAT == 2, !- Program Line 384\n", + " set ACST = ACSTaul*0.73+15.28+ACSTtol, !- Program Line 385\n", + " endif, !- Program Line 386\n", + " endif, !- Program Line 387\n", + " endif, !- Program Line 388\n", + " if (ComfStand == 4) && (ComfMod == 3), !- Program Line 389\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 390\n", + " if CAT == 1, !- Program Line 391\n", + " set AHST = PMOT*0.87+2.76+AHSTtol, !- Program Line 392\n", + " elseif CAT == 2, !- Program Line 393\n", + " set AHST = PMOT*0.91-0.48+AHSTtol, !- Program Line 394\n", + " endif, !- Program Line 395\n", + " elseif PMOT < AHSTall, !- Program Line 396\n", + " if CAT == 1, !- Program Line 397\n", + " set AHST = AHSTall*0.87+2.76+AHSTtol, !- Program Line 398\n", + " elseif CAT == 2, !- Program Line 399\n", + " set AHST = AHSTall*0.91-0.48+AHSTtol, !- Program Line 400\n", + " endif, !- Program Line 401\n", + " elseif PMOT > AHSTaul, !- Program Line 402\n", + " if CAT == 1, !- Program Line 403\n", + " set AHST = AHSTaul*0.87+2.76+AHSTtol, !- Program Line 404\n", + " elseif CAT == 2, !- Program Line 405\n", + " set AHST = AHSTaul*0.91-0.48+AHSTtol, !- Program Line 406\n", + " endif, !- Program Line 407\n", + " endif, !- Program Line 408\n", + " endif, !- Program Line 409\n", + " if (ComfStand == 5) && (ComfMod == 3), !- Program Line 410\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 411\n", + " if CAT == 1, !- Program Line 412\n", + " set ACST = PMOT*0.77+9.34+ACSTtol, !- Program Line 413\n", + " elseif CAT == 2, !- Program Line 414\n", + " set ACST = PMOT*0.73+12.72+ACSTtol, !- Program Line 415\n", + " endif, !- Program Line 416\n", + " elseif PMOT < ACSTall, !- Program Line 417\n", + " if CAT == 1, !- Program Line 418\n", + " set ACST = ACSTall*0.77+9.34+ACSTtol, !- Program Line 419\n", + " elseif CAT == 2, !- Program Line 420\n", + " set ACST = ACSTall*0.73+12.72+ACSTtol, !- Program Line 421\n", + " endif, !- Program Line 422\n", + " elseif PMOT > ACSTaul, !- Program Line 423\n", + " if CAT == 1, !- Program Line 424\n", + " set ACST = ACSTaul*0.77+9.34+ACSTtol, !- Program Line 425\n", + " elseif CAT == 2, !- Program Line 426\n", + " set ACST = ACSTaul*0.73+12.72+ACSTtol, !- Program Line 427\n", + " endif, !- Program Line 428\n", + " endif, !- Program Line 429\n", + " endif, !- Program Line 430\n", + " if (ComfStand == 5) && (ComfMod == 3), !- Program Line 431\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 432\n", + " if CAT == 1, !- Program Line 433\n", + " set AHST = PMOT*0.87-0.31+AHSTtol, !- Program Line 434\n", + " elseif CAT == 2, !- Program Line 435\n", + " set AHST = PMOT*0.91-3.69+AHSTtol, !- Program Line 436\n", + " endif, !- Program Line 437\n", + " elseif PMOT < AHSTall, !- Program Line 438\n", + " if CAT == 1, !- Program Line 439\n", + " set AHST = AHSTall*0.87-0.31+AHSTtol, !- Program Line 440\n", + " elseif CAT == 2, !- Program Line 441\n", + " set AHST = AHSTall*0.91-3.69+AHSTtol, !- Program Line 442\n", + " endif, !- Program Line 443\n", + " elseif PMOT > AHSTaul, !- Program Line 444\n", + " if CAT == 1, !- Program Line 445\n", + " set AHST = AHSTaul*0.87-0.31+AHSTtol, !- Program Line 446\n", + " elseif CAT == 2, !- Program Line 447\n", + " set AHST = AHSTaul*0.91-3.69+AHSTtol, !- Program Line 448\n", + " endif, !- Program Line 449\n", + " endif, !- Program Line 450\n", + " endif, !- Program Line 451\n", + " if (ComfStand == 6) && (ComfMod == 2), !- Program Line 452\n", + " if CAT==80, !- Program Line 453\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 454\n", + " set ACST = PMOT*0.30+25.9+ACSTtol, !- Program Line 455\n", + " elseif PMOT < ACSTall, !- Program Line 456\n", + " set ACST = 25+ACSTtol, !- Program Line 457\n", + " elseif PMOT > ACSTaul, !- Program Line 458\n", + " set ACST = 27+ACSTtol, !- Program Line 459\n", + " endif, !- Program Line 460\n", + " elseif CAT==90, !- Program Line 461\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 462\n", + " set ACST = PMOT*0.30+23.6+ACSTtol, !- Program Line 463\n", + " elseif PMOT < ACSTall, !- Program Line 464\n", + " set ACST = 24+ACSTtol, !- Program Line 465\n", + " elseif PMOT > ACSTaul, !- Program Line 466\n", + " set ACST = 26+ACSTtol, !- Program Line 467\n", + " endif, !- Program Line 468\n", + " endif, !- Program Line 469\n", + " endif, !- Program Line 470\n", + " if (ComfStand == 6) && (ComfMod == 2), !- Program Line 471\n", + " if CAT==80, !- Program Line 472\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 473\n", + " set AHST = PMOT*0.30+25.9+AHSTtol, !- Program Line 474\n", + " elseif PMOT < AHSTall, !- Program Line 475\n", + " set AHST = 19+AHSTtol, !- Program Line 476\n", + " elseif PMOT > AHSTaul, !- Program Line 477\n", + " set AHST = 22+AHSTtol, !- Program Line 478\n", + " endif, !- Program Line 479\n", + " elseif CAT==90, !- Program Line 480\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 481\n", + " set AHST = PMOT*0.30+23.6+AHSTtol, !- Program Line 482\n", + " elseif PMOT < AHSTall, !- Program Line 483\n", + " set AHST = 20+AHSTtol, !- Program Line 484\n", + " elseif PMOT > AHSTaul, !- Program Line 485\n", + " set AHST = 23+AHSTtol, !- Program Line 486\n", + " endif, !- Program Line 487\n", + " endif, !- Program Line 488\n", + " endif, !- Program Line 489\n", + " if (ComfStand == 6) && (ComfMod == 3), !- Program Line 490\n", + " if CAT == 80, !- Program Line 491\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 492\n", + " set ACST = PMOT*0.30+25.9+ACSTtol, !- Program Line 493\n", + " elseif (PMOT < ACSTall), !- Program Line 494\n", + " set ACST = ACSTall*0.30+25.9+ACSTtol, !- Program Line 495\n", + " elseif (PMOT > ACSTaul), !- Program Line 496\n", + " set ACST = ACSTaul*0.30+25.9+ACSTtol, !- Program Line 497\n", + " endif, !- Program Line 498\n", + " elseif CAT == 90, !- Program Line 499\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 500\n", + " set ACST = PMOT*0.30+23.6+ACSTtol, !- Program Line 501\n", + " elseif (PMOT < ACSTall), !- Program Line 502\n", + " set ACST = ACSTall*0.30+23.6+ACSTtol, !- Program Line 503\n", + " elseif (PMOT > ACSTaul), !- Program Line 504\n", + " set ACST = ACSTaul*0.30+23.6+ACSTtol, !- Program Line 505\n", + " endif, !- Program Line 506\n", + " endif, !- Program Line 507\n", + " endif, !- Program Line 508\n", + " if (ComfStand == 6) && (ComfMod == 3), !- Program Line 509\n", + " if CAT == 80, !- Program Line 510\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 511\n", + " set AHST = PMOT*0.32+14.88+AHSTtol, !- Program Line 512\n", + " elseif (PMOT < AHSTall), !- Program Line 513\n", + " set AHST = AHSTall*0.32+14.88+AHSTtol, !- Program Line 514\n", + " elseif (PMOT > AHSTaul), !- Program Line 515\n", + " set AHST = AHSTaul*0.32+14.88+AHSTtol, !- Program Line 516\n", + " endif, !- Program Line 517\n", + " elseif CAT == 90, !- Program Line 518\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 519\n", + " set AHST = PMOT*0.31+17.14+AHSTtol, !- Program Line 520\n", + " elseif (PMOT < AHSTall), !- Program Line 521\n", + " set AHST = AHSTall*0.31+17.14+AHSTtol, !- Program Line 522\n", + " elseif (PMOT > AHSTaul), !- Program Line 523\n", + " set AHST = AHSTaul*0.31+17.14+AHSTtol, !- Program Line 524\n", + " endif, !- Program Line 525\n", + " endif, !- Program Line 526\n", + " endif, !- Program Line 527\n", + " if ComfStand == 7 || ComfStand == 8 || ComfStand == 9 || ComfStand == 10, !- Program Line 528\n", + " if (ComfMod == 0), !- Program Line 529\n", + " if CAT==80, !- Program Line 530\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 531\n", + " elseif CAT==85, !- Program Line 532\n", + " set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 533\n", + " elseif CAT==90, !- Program Line 534\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 535\n", + " endif, !- Program Line 536\n", + " endif, !- Program Line 537\n", + " endif, !- Program Line 538\n", + " if ComfStand == 7 || ComfStand == 8 || ComfStand == 9 || ComfStand == 10, !- Program Line 539\n", + " if (ComfMod == 0), !- Program Line 540\n", + " if CAT==80, !- Program Line 541\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 542\n", + " elseif CAT==85, !- Program Line 543\n", + " set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 544\n", + " elseif CAT==90, !- Program Line 545\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 546\n", + " endif, !- Program Line 547\n", + " endif, !- Program Line 548\n", + " endif, !- Program Line 549\n", + " if (ComfStand == 7) && (ComfMod == 1), !- Program Line 550\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 551\n", + " set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 552\n", + " else, !- Program Line 553\n", + " if CAT==80, !- Program Line 554\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 555\n", + " elseif CAT==85, !- Program Line 556\n", + " set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 557\n", + " elseif CAT==90, !- Program Line 558\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 559\n", + " endif, !- Program Line 560\n", + " endif, !- Program Line 561\n", + " endif, !- Program Line 562\n", + " if (ComfStand == 7) && (ComfMod == 1), !- Program Line 563\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 564\n", + " set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 565\n", + " else, !- Program Line 566\n", + " if CAT==80, !- Program Line 567\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 568\n", + " elseif CAT==85, !- Program Line 569\n", + " set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 570\n", + " elseif CAT==90, !- Program Line 571\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 572\n", + " endif, !- Program Line 573\n", + " endif, !- Program Line 574\n", + " endif, !- Program Line 575\n", + " if (ComfStand == 7) && (ComfMod == 2), !- Program Line 576\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 577\n", + " set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 578\n", + " elseif CAT==80, !- Program Line 579\n", + " if PMOT < ACSTall, !- Program Line 580\n", + " set ACST = 25+ACSTtol, !- Program Line 581\n", + " elseif PMOT > ACSTaul, !- Program Line 582\n", + " set ACST = 27+ACSTtol, !- Program Line 583\n", + " endif, !- Program Line 584\n", + " elseif CAT==85, !- Program Line 585\n", + " if PMOT < ACSTall, !- Program Line 586\n", + " set ACST = 25.72+ACSTtol, !- Program Line 587\n", + " elseif PMOT > ACSTaul, !- Program Line 588\n", + " set ACST = 27.89+ACSTtol, !- Program Line 589\n", + " endif, !- Program Line 590\n", + " elseif CAT==90, !- Program Line 591\n", + " if PMOT < ACSTall, !- Program Line 592\n", + " set ACST = 24+ACSTtol, !- Program Line 593\n", + " elseif PMOT > ACSTaul, !- Program Line 594\n", + " set ACST = 26+ACSTtol, !- Program Line 595\n", + " endif, !- Program Line 596\n", + " endif, !- Program Line 597\n", + " endif, !- Program Line 598\n", + " if (ComfStand == 7) && (ComfMod == 2), !- Program Line 599\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 600\n", + " set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 601\n", + " elseif CAT==80, !- Program Line 602\n", + " if PMOT < AHSTall, !- Program Line 603\n", + " set AHST = 19+AHSTtol, !- Program Line 604\n", + " elseif PMOT > AHSTaul, !- Program Line 605\n", + " set AHST = 22+AHSTtol, !- Program Line 606\n", + " endif, !- Program Line 607\n", + " elseif CAT==85, !- Program Line 608\n", + " if PMOT < AHSTall, !- Program Line 609\n", + " set AHST = 20.77+AHSTtol, !- Program Line 610\n", + " elseif PMOT > AHSTaul, !- Program Line 611\n", + " set AHST = 24.26+AHSTtol, !- Program Line 612\n", + " endif, !- Program Line 613\n", + " elseif CAT==90, !- Program Line 614\n", + " if PMOT < AHSTall, !- Program Line 615\n", + " set AHST = 20+AHSTtol, !- Program Line 616\n", + " elseif PMOT > AHSTaul, !- Program Line 617\n", + " set AHST = 23+AHSTtol, !- Program Line 618\n", + " endif, !- Program Line 619\n", + " endif, !- Program Line 620\n", + " endif, !- Program Line 621\n", + " if (ComfStand == 7) && (ComfMod == 3), !- Program Line 622\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 623\n", + " set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 624\n", + " elseif PMOT < ACSTall, !- Program Line 625\n", + " set ACST = ACSTall*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 626\n", + " elseif PMOT > ACSTaul, !- Program Line 627\n", + " set ACST = ACSTaul*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 628\n", + " endif, !- Program Line 629\n", + " endif, !- Program Line 630\n", + " if (ComfStand == 7) && (ComfMod == 3), !- Program Line 631\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 632\n", + " set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 633\n", + " elseif PMOT < AHSTall, !- Program Line 634\n", + " set AHST = AHSTall*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 635\n", + " elseif PMOT > AHSTaul, !- Program Line 636\n", + " set AHST = AHSTaul*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 637\n", + " endif, !- Program Line 638\n", + " endif, !- Program Line 639\n", + " if (ComfStand == 8) && (ComfMod == 1), !- Program Line 640\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 641\n", + " set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 642\n", + " else, !- Program Line 643\n", + " if CAT==80, !- Program Line 644\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 645\n", + " elseif CAT==85, !- Program Line 646\n", + " set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 647\n", + " elseif CAT==90, !- Program Line 648\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 649\n", + " endif, !- Program Line 650\n", + " endif, !- Program Line 651\n", + " endif, !- Program Line 652\n", + " if (ComfStand == 8) && (ComfMod == 1), !- Program Line 653\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 654\n", + " set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 655\n", + " else, !- Program Line 656\n", + " if CAT==80, !- Program Line 657\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 658\n", + " elseif CAT==85, !- Program Line 659\n", + " set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 660\n", + " elseif CAT==90, !- Program Line 661\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 662\n", + " endif, !- Program Line 663\n", + " endif, !- Program Line 664\n", + " endif, !- Program Line 665\n", + " if (ComfStand == 8) && (ComfMod == 2), !- Program Line 666\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 667\n", + " set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 668\n", + " elseif CAT==80, !- Program Line 669\n", + " if PMOT < ACSTall, !- Program Line 670\n", + " set ACST = 25+ACSTtol, !- Program Line 671\n", + " elseif PMOT > ACSTaul, !- Program Line 672\n", + " set ACST = 27+ACSTtol, !- Program Line 673\n", + " endif, !- Program Line 674\n", + " elseif CAT==85, !- Program Line 675\n", + " if PMOT < ACSTall, !- Program Line 676\n", + " set ACST = 25.72+ACSTtol, !- Program Line 677\n", + " elseif PMOT > ACSTaul, !- Program Line 678\n", + " set ACST = 27.89+ACSTtol, !- Program Line 679\n", + " endif, !- Program Line 680\n", + " elseif CAT==90, !- Program Line 681\n", + " if PMOT < ACSTall, !- Program Line 682\n", + " set ACST = 24+ACSTtol, !- Program Line 683\n", + " elseif PMOT > ACSTaul, !- Program Line 684\n", + " set ACST = 26+ACSTtol, !- Program Line 685\n", + " endif, !- Program Line 686\n", + " endif, !- Program Line 687\n", + " endif, !- Program Line 688\n", + " if (ComfStand == 8) && (ComfMod == 2), !- Program Line 689\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 690\n", + " set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 691\n", + " elseif CAT==80, !- Program Line 692\n", + " if PMOT < AHSTall, !- Program Line 693\n", + " set AHST = 19+AHSTtol, !- Program Line 694\n", + " elseif PMOT > AHSTaul, !- Program Line 695\n", + " set AHST = 22+AHSTtol, !- Program Line 696\n", + " endif, !- Program Line 697\n", + " elseif CAT==85, !- Program Line 698\n", + " if PMOT < AHSTall, !- Program Line 699\n", + " set AHST = 20.77+AHSTtol, !- Program Line 700\n", + " elseif PMOT > AHSTaul, !- Program Line 701\n", + " set AHST = 24.26+AHSTtol, !- Program Line 702\n", + " endif, !- Program Line 703\n", + " elseif CAT==90, !- Program Line 704\n", + " if PMOT < AHSTall, !- Program Line 705\n", + " set AHST = 20+AHSTtol, !- Program Line 706\n", + " elseif PMOT > AHSTaul, !- Program Line 707\n", + " set AHST = 23+AHSTtol, !- Program Line 708\n", + " endif, !- Program Line 709\n", + " endif, !- Program Line 710\n", + " endif, !- Program Line 711\n", + " if (ComfStand == 8) && (ComfMod == 3), !- Program Line 712\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 713\n", + " set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 714\n", + " elseif PMOT < ACSTall, !- Program Line 715\n", + " set ACST = ACSTall*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 716\n", + " elseif PMOT > ACSTaul, !- Program Line 717\n", + " set ACST = ACSTaul*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 718\n", + " endif, !- Program Line 719\n", + " endif, !- Program Line 720\n", + " if (ComfStand == 8) && (ComfMod == 3), !- Program Line 721\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 722\n", + " set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 723\n", + " elseif PMOT < AHSTall, !- Program Line 724\n", + " set AHST = AHSTall*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 725\n", + " elseif PMOT > AHSTaul, !- Program Line 726\n", + " set AHST = AHSTaul*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 727\n", + " endif, !- Program Line 728\n", + " endif, !- Program Line 729\n", + " if (ComfStand == 9) && (ComfMod == 1), !- Program Line 730\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 731\n", + " set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 732\n", + " else, !- Program Line 733\n", + " if CAT==80, !- Program Line 734\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 735\n", + " elseif CAT==90, !- Program Line 736\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 737\n", + " endif, !- Program Line 738\n", + " endif, !- Program Line 739\n", + " endif, !- Program Line 740\n", + " if (ComfStand == 9) && (ComfMod == 1), !- Program Line 741\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 742\n", + " set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 743\n", + " else, !- Program Line 744\n", + " if CAT==80, !- Program Line 745\n", + " set AHST = PMOT*0.078+23.25+2.72+AHSTtol, !- Program Line 746\n", + " elseif CAT==90, !- Program Line 747\n", + " set AHST = PMOT*0.078+23.25+1.5+AHSTtol, !- Program Line 748\n", + " endif, !- Program Line 749\n", + " endif, !- Program Line 750\n", + " endif, !- Program Line 751\n", + " if (ComfStand == 9) && (ComfMod == 2), !- Program Line 752\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 753\n", + " set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 754\n", + " elseif CAT==80, !- Program Line 755\n", + " if PMOT < ACSTall, !- Program Line 756\n", + " set ACST = 25+ACSTtol, !- Program Line 757\n", + " elseif PMOT > ACSTaul, !- Program Line 758\n", + " set ACST = 27+ACSTtol, !- Program Line 759\n", + " endif, !- Program Line 760\n", + " elseif CAT==90, !- Program Line 761\n", + " if PMOT < ACSTall, !- Program Line 762\n", + " set ACST = 24+ACSTtol, !- Program Line 763\n", + " elseif PMOT > ACSTaul, !- Program Line 764\n", + " set ACST = 26+ACSTtol, !- Program Line 765\n", + " endif, !- Program Line 766\n", + " endif, !- Program Line 767\n", + " endif, !- Program Line 768\n", + " if (ComfStand == 9) && (ComfMod == 2), !- Program Line 769\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 770\n", + " set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 771\n", + " elseif CAT==80, !- Program Line 772\n", + " if PMOT < AHSTall, !- Program Line 773\n", + " set AHST = 19+AHSTtol, !- Program Line 774\n", + " elseif PMOT > AHSTaul, !- Program Line 775\n", + " set AHST = 22+AHSTtol, !- Program Line 776\n", + " endif, !- Program Line 777\n", + " elseif CAT==90, !- Program Line 778\n", + " if PMOT < AHSTall, !- Program Line 779\n", + " set AHST = 20+AHSTtol, !- Program Line 780\n", + " elseif PMOT > AHSTaul, !- Program Line 781\n", + " set AHST = 23+AHSTtol, !- Program Line 782\n", + " endif, !- Program Line 783\n", + " endif, !- Program Line 784\n", + " endif, !- Program Line 785\n", + " if (ComfStand == 9) && (ComfMod == 3), !- Program Line 786\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 787\n", + " set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 788\n", + " elseif PMOT < ACSTall, !- Program Line 789\n", + " set ACST = ACSTall*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 790\n", + " elseif PMOT > ACSTaul, !- Program Line 791\n", + " set ACST = ACSTaul*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 792\n", + " endif, !- Program Line 793\n", + " endif, !- Program Line 794\n", + " if (ComfStand == 9) && (ComfMod == 3), !- Program Line 795\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 796\n", + " set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 797\n", + " elseif PMOT < AHSTall, !- Program Line 798\n", + " set AHST = AHSTall*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 799\n", + " elseif PMOT > AHSTaul, !- Program Line 800\n", + " set AHST = AHSTaul*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 801\n", + " endif, !- Program Line 802\n", + " endif, !- Program Line 803\n", + " if (ComfStand == 10) && (ComfMod == 1), !- Program Line 804\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 805\n", + " set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 806\n", + " else, !- Program Line 807\n", + " if CAT==80, !- Program Line 808\n", + " set ACST = RMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 809\n", + " elseif CAT==90, !- Program Line 810\n", + " set ACST = RMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 811\n", + " endif, !- Program Line 812\n", + " endif, !- Program Line 813\n", + " endif, !- Program Line 814\n", + " if (ComfStand == 10) && (ComfMod == 1), !- Program Line 815\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 816\n", + " set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 817\n", + " else, !- Program Line 818\n", + " if CAT==80, !- Program Line 819\n", + " set AHST = RMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 820\n", + " elseif CAT==90, !- Program Line 821\n", + " set AHST = RMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 822\n", + " endif, !- Program Line 823\n", + " endif, !- Program Line 824\n", + " endif, !- Program Line 825\n", + " if (ComfStand == 10) && (ComfMod == 2), !- Program Line 826\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 827\n", + " set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 828\n", + " elseif CAT==80, !- Program Line 829\n", + " if RMOT < ACSTall, !- Program Line 830\n", + " set ACST = 25+ACSTtol, !- Program Line 831\n", + " elseif RMOT > ACSTaul, !- Program Line 832\n", + " set ACST = 27+ACSTtol, !- Program Line 833\n", + " endif, !- Program Line 834\n", + " elseif CAT==90, !- Program Line 835\n", + " if RMOT < ACSTall, !- Program Line 836\n", + " set ACST = 24+ACSTtol, !- Program Line 837\n", + " elseif RMOT > ACSTaul, !- Program Line 838\n", + " set ACST = 26+ACSTtol, !- Program Line 839\n", + " endif, !- Program Line 840\n", + " endif, !- Program Line 841\n", + " endif, !- Program Line 842\n", + " if (ComfStand == 10) && (ComfMod == 2), !- Program Line 843\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 844\n", + " set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 845\n", + " elseif CAT==80, !- Program Line 846\n", + " if RMOT < AHSTall, !- Program Line 847\n", + " set AHST = 19+AHSTtol, !- Program Line 848\n", + " elseif RMOT > AHSTaul, !- Program Line 849\n", + " set AHST = 22+AHSTtol, !- Program Line 850\n", + " endif, !- Program Line 851\n", + " elseif CAT==90, !- Program Line 852\n", + " if RMOT < AHSTall, !- Program Line 853\n", + " set AHST = 20+AHSTtol, !- Program Line 854\n", + " elseif RMOT > AHSTaul, !- Program Line 855\n", + " set AHST = 23+AHSTtol, !- Program Line 856\n", + " endif, !- Program Line 857\n", + " endif, !- Program Line 858\n", + " endif, !- Program Line 859\n", + " if (ComfStand == 10) && (ComfMod == 3), !- Program Line 860\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 861\n", + " set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 862\n", + " elseif RMOT < ACSTall, !- Program Line 863\n", + " set ACST = ACSTall*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 864\n", + " elseif RMOT > ACSTaul, !- Program Line 865\n", + " set ACST = ACSTaul*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 866\n", + " endif, !- Program Line 867\n", + " endif, !- Program Line 868\n", + " if (ComfStand == 10) && (ComfMod == 3), !- Program Line 869\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 870\n", + " set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 871\n", + " elseif RMOT < AHSTall, !- Program Line 872\n", + " set AHST = AHSTall*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 873\n", + " elseif RMOT > AHSTaul, !- Program Line 874\n", + " set AHST = AHSTaul*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 875\n", + " endif, !- Program Line 876\n", + " endif, !- Program Line 877\n", + " if (ComfStand == 11) && (ComfMod == 1), !- Program Line 878\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 879\n", + " set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 880\n", + " else, !- Program Line 881\n", + " if CAT==80, !- Program Line 882\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 883\n", + " elseif CAT==90, !- Program Line 884\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 885\n", + " endif, !- Program Line 886\n", + " endif, !- Program Line 887\n", + " endif, !- Program Line 888\n", + " if (ComfStand == 11) && (ComfMod == 1), !- Program Line 889\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 890\n", + " set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 891\n", + " else, !- Program Line 892\n", + " if CAT==80, !- Program Line 893\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 894\n", + " elseif CAT==90, !- Program Line 895\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 896\n", + " endif, !- Program Line 897\n", + " endif, !- Program Line 898\n", + " endif, !- Program Line 899\n", + " if (ComfStand == 11) && (ComfMod == 2), !- Program Line 900\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 901\n", + " set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 902\n", + " elseif CAT==80, !- Program Line 903\n", + " if PMOT < ACSTall, !- Program Line 904\n", + " set ACST = 25+ACSTtol, !- Program Line 905\n", + " elseif PMOT > ACSTaul, !- Program Line 906\n", + " set ACST = 27+ACSTtol, !- Program Line 907\n", + " endif, !- Program Line 908\n", + " elseif CAT==90, !- Program Line 909\n", + " if PMOT < ACSTall, !- Program Line 910\n", + " set ACST = 24+ACSTtol, !- Program Line 911\n", + " elseif PMOT > ACSTaul, !- Program Line 912\n", + " set ACST = 26+ACSTtol, !- Program Line 913\n", + " endif, !- Program Line 914\n", + " endif, !- Program Line 915\n", + " endif, !- Program Line 916\n", + " if (ComfStand == 11) && (ComfMod == 2), !- Program Line 917\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 918\n", + " set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 919\n", + " elseif CAT==80, !- Program Line 920\n", + " if PMOT < AHSTall, !- Program Line 921\n", + " set AHST = 19+AHSTtol, !- Program Line 922\n", + " elseif PMOT > AHSTaul, !- Program Line 923\n", + " set AHST = 22+AHSTtol, !- Program Line 924\n", + " endif, !- Program Line 925\n", + " elseif CAT==90, !- Program Line 926\n", + " if PMOT < AHSTall, !- Program Line 927\n", + " set AHST = 20+AHSTtol, !- Program Line 928\n", + " elseif PMOT > AHSTaul, !- Program Line 929\n", + " set AHST = 23+AHSTtol, !- Program Line 930\n", + " endif, !- Program Line 931\n", + " endif, !- Program Line 932\n", + " endif, !- Program Line 933\n", + " if (ComfStand == 11) && (ComfMod == 3), !- Program Line 934\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 935\n", + " set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 936\n", + " elseif PMOT < ACSTall, !- Program Line 937\n", + " set ACST = ACSTall*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 938\n", + " elseif PMOT > ACSTaul, !- Program Line 939\n", + " set ACST = ACSTaul*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 940\n", + " endif, !- Program Line 941\n", + " endif, !- Program Line 942\n", + " if (ComfStand == 11) && (ComfMod == 3), !- Program Line 943\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 944\n", + " set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 945\n", + " elseif PMOT < AHSTall, !- Program Line 946\n", + " set AHST = AHSTall*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 947\n", + " elseif PMOT > AHSTaul, !- Program Line 948\n", + " set AHST = AHSTaul*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 949\n", + " endif, !- Program Line 950\n", + " endif, !- Program Line 951\n", + " if (ComfStand == 12) && (ComfMod == 0), !- Program Line 952\n", + " if (CAT==80), !- Program Line 953\n", + " set ACST = 27+ACSTtol, !- Program Line 954\n", + " elseif (CAT==90), !- Program Line 955\n", + " set ACST = 25.5+ACSTtol, !- Program Line 956\n", + " endif, !- Program Line 957\n", + " endif, !- Program Line 958\n", + " if (ComfStand == 12) && (ComfMod == 0), !- Program Line 959\n", + " if (CAT==80), !- Program Line 960\n", + " set AHST = 20+AHSTtol, !- Program Line 961\n", + " elseif (CAT==90), !- Program Line 962\n", + " set AHST = 21.5+AHSTtol, !- Program Line 963\n", + " endif, !- Program Line 964\n", + " endif, !- Program Line 965\n", + " if (ComfStand == 12) && (ComfMod == 1), !- Program Line 966\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 967\n", + " set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 968\n", + " elseif CAT==80, !- Program Line 969\n", + " if PMOT < ACSTall, !- Program Line 970\n", + " set ACST = 27+ACSTtol, !- Program Line 971\n", + " elseif PMOT > ACSTaul, !- Program Line 972\n", + " set ACST = 27+ACSTtol, !- Program Line 973\n", + " endif, !- Program Line 974\n", + " elseif CAT==90, !- Program Line 975\n", + " if PMOT < ACSTall, !- Program Line 976\n", + " set ACST = 25.5+ACSTtol, !- Program Line 977\n", + " elseif PMOT > ACSTaul, !- Program Line 978\n", + " set ACST = 25.5+ACSTtol, !- Program Line 979\n", + " endif, !- Program Line 980\n", + " endif, !- Program Line 981\n", + " endif, !- Program Line 982\n", + " if (ComfStand == 12) && (ComfMod == 1), !- Program Line 983\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 984\n", + " set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 985\n", + " elseif CAT==80, !- Program Line 986\n", + " if PMOT < AHSTall, !- Program Line 987\n", + " set AHST = 20+AHSTtol, !- Program Line 988\n", + " elseif PMOT > AHSTaul, !- Program Line 989\n", + " set AHST = 20+AHSTtol, !- Program Line 990\n", + " endif, !- Program Line 991\n", + " elseif CAT==90, !- Program Line 992\n", + " if PMOT < AHSTall, !- Program Line 993\n", + " set AHST = 21.5+AHSTtol, !- Program Line 994\n", + " elseif PMOT > AHSTaul, !- Program Line 995\n", + " set AHST = 21.5+AHSTtol, !- Program Line 996\n", + " endif, !- Program Line 997\n", + " endif, !- Program Line 998\n", + " endif, !- Program Line 999\n", + " if (ComfStand == 12) && (ComfMod == 2), !- Program Line 1000\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1001\n", + " set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1002\n", + " elseif CAT==80, !- Program Line 1003\n", + " if PMOT < ACSTall, !- Program Line 1004\n", + " set ACST = 25+ACSTtol, !- Program Line 1005\n", + " elseif PMOT > ACSTaul, !- Program Line 1006\n", + " set ACST = 27+ACSTtol, !- Program Line 1007\n", + " endif, !- Program Line 1008\n", + " elseif CAT==90, !- Program Line 1009\n", + " if PMOT < ACSTall, !- Program Line 1010\n", + " set ACST = 24+ACSTtol, !- Program Line 1011\n", + " elseif PMOT > ACSTaul, !- Program Line 1012\n", + " set ACST = 26+ACSTtol, !- Program Line 1013\n", + " endif, !- Program Line 1014\n", + " endif, !- Program Line 1015\n", + " endif, !- Program Line 1016\n", + " if (ComfStand == 12) && (ComfMod == 2), !- Program Line 1017\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1018\n", + " set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1019\n", + " elseif CAT==80, !- Program Line 1020\n", + " if PMOT < AHSTall, !- Program Line 1021\n", + " set AHST = 19+AHSTtol, !- Program Line 1022\n", + " elseif PMOT > AHSTaul, !- Program Line 1023\n", + " set AHST = 22+AHSTtol, !- Program Line 1024\n", + " endif, !- Program Line 1025\n", + " elseif CAT==90, !- Program Line 1026\n", + " if PMOT < AHSTall, !- Program Line 1027\n", + " set AHST = 20+AHSTtol, !- Program Line 1028\n", + " elseif PMOT > AHSTaul, !- Program Line 1029\n", + " set AHST = 23+AHSTtol, !- Program Line 1030\n", + " endif, !- Program Line 1031\n", + " endif, !- Program Line 1032\n", + " endif, !- Program Line 1033\n", + " if (ComfStand == 12) && (ComfMod == 3), !- Program Line 1034\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1035\n", + " set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1036\n", + " elseif PMOT < ACSTall, !- Program Line 1037\n", + " set ACST = ACSTall*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1038\n", + " elseif PMOT > ACSTaul, !- Program Line 1039\n", + " set ACST = ACSTaul*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1040\n", + " endif, !- Program Line 1041\n", + " endif, !- Program Line 1042\n", + " if (ComfStand == 12) && (ComfMod == 3), !- Program Line 1043\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1044\n", + " set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1045\n", + " elseif PMOT < AHSTall, !- Program Line 1046\n", + " set AHST = AHSTall*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1047\n", + " elseif PMOT > AHSTaul, !- Program Line 1048\n", + " set AHST = AHSTaul*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1049\n", + " endif, !- Program Line 1050\n", + " endif, !- Program Line 1051\n", + " if (ComfStand == 13) || (ComfStand == 14), !- Program Line 1052\n", + " if (CAT==80), !- Program Line 1053\n", + " if (CurrentTime > 6) && (CurrentTime < 23), !- Program Line 1054\n", + " if (ComfMod == 0.1), !- Program Line 1055\n", + " set ACST = 27+ACSTtol, !- Program Line 1056\n", + " elseif (ComfMod == 0.2), !- Program Line 1057\n", + " set ACST = 26+ACSTtol, !- Program Line 1058\n", + " elseif (ComfMod == 0.3), !- Program Line 1059\n", + " set ACST = 25+ACSTtol, !- Program Line 1060\n", + " elseif (ComfMod == 0.4), !- Program Line 1061\n", + " set ACST = 24+ACSTtol, !- Program Line 1062\n", + " elseif (ComfMod == 0.5), !- Program Line 1063\n", + " set ACST = 23+ACSTtol, !- Program Line 1064\n", + " endif, !- Program Line 1065\n", + " else, !- Program Line 1066\n", + " set ACST = 24+ACSTtol, !- Program Line 1067\n", + " endif, !- Program Line 1068\n", + " elseif (CAT==90), !- Program Line 1069\n", + " if (CurrentTime > 6) && (CurrentTime < 23), !- Program Line 1070\n", + " if (ComfMod == 0.1), !- Program Line 1071\n", + " set ACST = 26+ACSTtol, !- Program Line 1072\n", + " elseif (ComfMod == 0.2), !- Program Line 1073\n", + " set ACST = 25+ACSTtol, !- Program Line 1074\n", + " elseif (ComfMod == 0.3), !- Program Line 1075\n", + " set ACST = 24+ACSTtol, !- Program Line 1076\n", + " elseif (ComfMod == 0.4), !- Program Line 1077\n", + " set ACST = 23+ACSTtol, !- Program Line 1078\n", + " elseif (ComfMod == 0.5), !- Program Line 1079\n", + " set ACST = 22+ACSTtol, !- Program Line 1080\n", + " endif, !- Program Line 1081\n", + " else, !- Program Line 1082\n", + " set ACST = 23+ACSTtol, !- Program Line 1083\n", + " endif, !- Program Line 1084\n", + " endif, !- Program Line 1085\n", + " endif, !- Program Line 1086\n", + " if (ComfStand == 13) || (ComfStand == 14), !- Program Line 1087\n", + " if (ComfMod == 0.1) || (ComfMod == 0.2) || (ComfMod == 0.3) || (ComfMod == 0.4) || (ComfMod == 0.5), !- Program Line 1088\n", + " if (CAT==80), !- Program Line 1089\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1090\n", + " set AHST = 20+AHSTtol, !- Program Line 1091\n", + " else, !- Program Line 1092\n", + " set AHST = 18+AHSTtol, !- Program Line 1093\n", + " endif, !- Program Line 1094\n", + " elseif (CAT==90), !- Program Line 1095\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1096\n", + " set AHST = 21+AHSTtol, !- Program Line 1097\n", + " else, !- Program Line 1098\n", + " set AHST = 19+AHSTtol, !- Program Line 1099\n", + " endif, !- Program Line 1100\n", + " endif, !- Program Line 1101\n", + " endif, !- Program Line 1102\n", + " endif, !- Program Line 1103\n", + " if (ComfStand == 13), !- Program Line 1104\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1105\n", + " if CAT == 80, !- Program Line 1106\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1107\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1108\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1109\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1110\n", + " set ACST = 27+ACSTtol, !- Program Line 1111\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1112\n", + " set ACST = 26+ACSTtol, !- Program Line 1113\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1114\n", + " set ACST = 25+ACSTtol, !- Program Line 1115\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1116\n", + " set ACST = 24+ACSTtol, !- Program Line 1117\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1118\n", + " set ACST = 23+ACSTtol, !- Program Line 1119\n", + " else, !- Program Line 1120\n", + " set ACST = 24+ACSTtol, !- Program Line 1121\n", + " endif, !- Program Line 1122\n", + " endif, !- Program Line 1123\n", + " elseif CAT==90, !- Program Line 1124\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1125\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1126\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1127\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1128\n", + " set ACST = 26+ACSTtol, !- Program Line 1129\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1130\n", + " set ACST = 25+ACSTtol, !- Program Line 1131\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1132\n", + " set ACST = 24+ACSTtol, !- Program Line 1133\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1134\n", + " set ACST = 23+ACSTtol, !- Program Line 1135\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1136\n", + " set ACST = 22+ACSTtol, !- Program Line 1137\n", + " else, !- Program Line 1138\n", + " set ACST = 23+ACSTtol, !- Program Line 1139\n", + " endif, !- Program Line 1140\n", + " endif, !- Program Line 1141\n", + " endif, !- Program Line 1142\n", + " endif, !- Program Line 1143\n", + " endif, !- Program Line 1144\n", + " if (ComfStand == 13), !- Program Line 1145\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1146\n", + " if CAT == 80, !- Program Line 1147\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1148\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1149\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1150\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1151\n", + " set AHST = 20+AHSTtol, !- Program Line 1152\n", + " else, !- Program Line 1153\n", + " set AHST = 18+AHSTtol, !- Program Line 1154\n", + " endif, !- Program Line 1155\n", + " endif, !- Program Line 1156\n", + " elseif CAT==90, !- Program Line 1157\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1158\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1159\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1160\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1161\n", + " set AHST = 21+AHSTtol, !- Program Line 1162\n", + " else, !- Program Line 1163\n", + " set AHST = 19+AHSTtol, !- Program Line 1164\n", + " endif, !- Program Line 1165\n", + " endif, !- Program Line 1166\n", + " endif, !- Program Line 1167\n", + " endif, !- Program Line 1168\n", + " endif, !- Program Line 1169\n", + " if (ComfStand == 13) && (ComfMod == 2), !- Program Line 1170\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1171\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1172\n", + " elseif CAT==80, !- Program Line 1173\n", + " if PMOT < ACSTall, !- Program Line 1174\n", + " set ACST = 25+ACSTtol, !- Program Line 1175\n", + " elseif PMOT > ACSTaul, !- Program Line 1176\n", + " set ACST = 27+ACSTtol, !- Program Line 1177\n", + " endif, !- Program Line 1178\n", + " elseif CAT==90, !- Program Line 1179\n", + " if PMOT < ACSTall, !- Program Line 1180\n", + " set ACST = 24+ACSTtol, !- Program Line 1181\n", + " elseif PMOT > ACSTaul, !- Program Line 1182\n", + " set ACST = 26+ACSTtol, !- Program Line 1183\n", + " endif, !- Program Line 1184\n", + " endif, !- Program Line 1185\n", + " endif, !- Program Line 1186\n", + " if (ComfStand == 13) && (ComfMod == 2), !- Program Line 1187\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1188\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1189\n", + " elseif CAT==80, !- Program Line 1190\n", + " if PMOT < AHSTall, !- Program Line 1191\n", + " set AHST = 19+AHSTtol, !- Program Line 1192\n", + " elseif PMOT > AHSTaul, !- Program Line 1193\n", + " set AHST = 22+AHSTtol, !- Program Line 1194\n", + " endif, !- Program Line 1195\n", + " elseif CAT==90, !- Program Line 1196\n", + " if PMOT < AHSTall, !- Program Line 1197\n", + " set AHST = 20+AHSTtol, !- Program Line 1198\n", + " elseif PMOT > AHSTaul, !- Program Line 1199\n", + " set AHST = 23+AHSTtol, !- Program Line 1200\n", + " endif, !- Program Line 1201\n", + " endif, !- Program Line 1202\n", + " endif, !- Program Line 1203\n", + " if (ComfStand == 13) && (ComfMod == 3), !- Program Line 1204\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1205\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1206\n", + " elseif PMOT < ACSTall, !- Program Line 1207\n", + " set ACST = ACSTall*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1208\n", + " elseif PMOT > ACSTaul, !- Program Line 1209\n", + " set ACST = ACSTaul*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1210\n", + " endif, !- Program Line 1211\n", + " endif, !- Program Line 1212\n", + " if (ComfStand == 13) && (ComfMod == 3), !- Program Line 1213\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1214\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1215\n", + " elseif PMOT < AHSTall, !- Program Line 1216\n", + " set AHST = AHSTall*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1217\n", + " elseif PMOT > AHSTaul, !- Program Line 1218\n", + " set AHST = AHSTaul*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1219\n", + " endif, !- Program Line 1220\n", + " endif, !- Program Line 1221\n", + " if (ComfStand == 14), !- Program Line 1222\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1223\n", + " if CAT == 80, !- Program Line 1224\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1225\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1226\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1227\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1228\n", + " set ACST = 27+ACSTtol, !- Program Line 1229\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1230\n", + " set ACST = 26+ACSTtol, !- Program Line 1231\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1232\n", + " set ACST = 25+ACSTtol, !- Program Line 1233\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1234\n", + " set ACST = 24+ACSTtol, !- Program Line 1235\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1236\n", + " set ACST = 23+ACSTtol, !- Program Line 1237\n", + " else, !- Program Line 1238\n", + " set ACST = 24+ACSTtol, !- Program Line 1239\n", + " endif, !- Program Line 1240\n", + " endif, !- Program Line 1241\n", + " elseif CAT==90, !- Program Line 1242\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1243\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1244\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1245\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1246\n", + " set ACST = 26+ACSTtol, !- Program Line 1247\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1248\n", + " set ACST = 25+ACSTtol, !- Program Line 1249\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1250\n", + " set ACST = 24+ACSTtol, !- Program Line 1251\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1252\n", + " set ACST = 23+ACSTtol, !- Program Line 1253\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1254\n", + " set ACST = 22+ACSTtol, !- Program Line 1255\n", + " else, !- Program Line 1256\n", + " set ACST = 23+ACSTtol, !- Program Line 1257\n", + " endif, !- Program Line 1258\n", + " endif, !- Program Line 1259\n", + " endif, !- Program Line 1260\n", + " endif, !- Program Line 1261\n", + " endif, !- Program Line 1262\n", + " if (ComfStand == 14), !- Program Line 1263\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1264\n", + " if CAT == 80, !- Program Line 1265\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1266\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1267\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1268\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1269\n", + " set AHST = 20+AHSTtol, !- Program Line 1270\n", + " else, !- Program Line 1271\n", + " set AHST = 18+AHSTtol, !- Program Line 1272\n", + " endif, !- Program Line 1273\n", + " endif, !- Program Line 1274\n", + " elseif CAT==90, !- Program Line 1275\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1276\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1277\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1278\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1279\n", + " set AHST = 21+AHSTtol, !- Program Line 1280\n", + " else, !- Program Line 1281\n", + " set AHST = 19+AHSTtol, !- Program Line 1282\n", + " endif, !- Program Line 1283\n", + " endif, !- Program Line 1284\n", + " endif, !- Program Line 1285\n", + " endif, !- Program Line 1286\n", + " endif, !- Program Line 1287\n", + " if (ComfStand == 14) && (ComfMod == 2), !- Program Line 1288\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1289\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1290\n", + " elseif CAT==80, !- Program Line 1291\n", + " if PMOT < ACSTall, !- Program Line 1292\n", + " set ACST = 25+ACSTtol, !- Program Line 1293\n", + " elseif PMOT > ACSTaul, !- Program Line 1294\n", + " set ACST = 27+ACSTtol, !- Program Line 1295\n", + " endif, !- Program Line 1296\n", + " elseif CAT==90, !- Program Line 1297\n", + " if PMOT < ACSTall, !- Program Line 1298\n", + " set ACST = 24+ACSTtol, !- Program Line 1299\n", + " elseif PMOT > ACSTaul, !- Program Line 1300\n", + " set ACST = 26+ACSTtol, !- Program Line 1301\n", + " endif, !- Program Line 1302\n", + " endif, !- Program Line 1303\n", + " endif, !- Program Line 1304\n", + " if (ComfStand == 14) && (ComfMod == 2), !- Program Line 1305\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1306\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1307\n", + " elseif CAT==80, !- Program Line 1308\n", + " if PMOT < AHSTall, !- Program Line 1309\n", + " set AHST = 19+AHSTtol, !- Program Line 1310\n", + " elseif PMOT > AHSTaul, !- Program Line 1311\n", + " set AHST = 22+AHSTtol, !- Program Line 1312\n", + " endif, !- Program Line 1313\n", + " elseif CAT==90, !- Program Line 1314\n", + " if PMOT < AHSTall, !- Program Line 1315\n", + " set AHST = 20+AHSTtol, !- Program Line 1316\n", + " elseif PMOT > AHSTaul, !- Program Line 1317\n", + " set AHST = 23+AHSTtol, !- Program Line 1318\n", + " endif, !- Program Line 1319\n", + " endif, !- Program Line 1320\n", + " endif, !- Program Line 1321\n", + " if (ComfStand == 14) && (ComfMod == 3), !- Program Line 1322\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1323\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1324\n", + " elseif PMOT < ACSTall, !- Program Line 1325\n", + " set ACST = ACSTall*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1326\n", + " elseif PMOT > ACSTaul, !- Program Line 1327\n", + " set ACST = ACSTaul*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1328\n", + " endif, !- Program Line 1329\n", + " endif, !- Program Line 1330\n", + " if (ComfStand == 14) && (ComfMod == 3), !- Program Line 1331\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1332\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1333\n", + " elseif PMOT < AHSTall, !- Program Line 1334\n", + " set AHST = AHSTall*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1335\n", + " elseif PMOT > AHSTaul, !- Program Line 1336\n", + " set AHST = AHSTaul*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1337\n", + " endif, !- Program Line 1338\n", + " endif, !- Program Line 1339\n", + " if (ComfStand == 15) || (ComfStand == 16), !- Program Line 1340\n", + " if (ComfMod == 0), !- Program Line 1341\n", + " if (CAT==80), !- Program Line 1342\n", + " if PMOT < 20, !- Program Line 1343\n", + " set ACST = 23.5+ACSTtol, !- Program Line 1344\n", + " set AHST = 21+ACSTtol, !- Program Line 1345\n", + " else, !- Program Line 1346\n", + " set ACST = 25.5+ACSTtol, !- Program Line 1347\n", + " set AHST = 22.5+ACSTtol, !- Program Line 1348\n", + " endif, !- Program Line 1349\n", + " elseif (CAT==90), !- Program Line 1350\n", + " if PMOT < 20, !- Program Line 1351\n", + " set ACST = 23+ACSTtol, !- Program Line 1352\n", + " set AHST = 21.5+ACSTtol, !- Program Line 1353\n", + " else, !- Program Line 1354\n", + " set ACST = 25+ACSTtol, !- Program Line 1355\n", + " set AHST = 23+ACSTtol, !- Program Line 1356\n", + " endif, !- Program Line 1357\n", + " endif, !- Program Line 1358\n", + " endif, !- Program Line 1359\n", + " endif, !- Program Line 1360\n", + " if (ComfStand == 15) && (ComfMod == 1), !- Program Line 1361\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1362\n", + " set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1363\n", + " elseif CAT==80, !- Program Line 1364\n", + " if PMOT < ACSTall, !- Program Line 1365\n", + " set ACST = 23.5+ACSTtol, !- Program Line 1366\n", + " elseif PMOT > ACSTaul, !- Program Line 1367\n", + " set ACST = 25.5+ACSTtol, !- Program Line 1368\n", + " endif, !- Program Line 1369\n", + " elseif CAT==90, !- Program Line 1370\n", + " if PMOT < ACSTall, !- Program Line 1371\n", + " set ACST = 23+ACSTtol, !- Program Line 1372\n", + " elseif PMOT > ACSTaul, !- Program Line 1373\n", + " set ACST = 25+ACSTtol, !- Program Line 1374\n", + " endif, !- Program Line 1375\n", + " endif, !- Program Line 1376\n", + " endif, !- Program Line 1377\n", + " if (ComfStand == 15) && (ComfMod == 1), !- Program Line 1378\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1379\n", + " set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1380\n", + " elseif CAT==80, !- Program Line 1381\n", + " if PMOT < AHSTall, !- Program Line 1382\n", + " set AHST = 21+AHSTtol, !- Program Line 1383\n", + " elseif PMOT > AHSTaul, !- Program Line 1384\n", + " set AHST = 22.5+AHSTtol, !- Program Line 1385\n", + " endif, !- Program Line 1386\n", + " elseif CAT==90, !- Program Line 1387\n", + " if PMOT < AHSTall, !- Program Line 1388\n", + " set AHST = 23+AHSTtol, !- Program Line 1389\n", + " elseif PMOT > AHSTaul, !- Program Line 1390\n", + " set AHST = 23+AHSTtol, !- Program Line 1391\n", + " endif, !- Program Line 1392\n", + " endif, !- Program Line 1393\n", + " endif, !- Program Line 1394\n", + " if (ComfStand == 15) && (ComfMod == 2), !- Program Line 1395\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1396\n", + " set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1397\n", + " elseif CAT==80, !- Program Line 1398\n", + " if PMOT < ACSTall, !- Program Line 1399\n", + " set ACST = 25+ACSTtol, !- Program Line 1400\n", + " elseif PMOT > ACSTaul, !- Program Line 1401\n", + " set ACST = 27+ACSTtol, !- Program Line 1402\n", + " endif, !- Program Line 1403\n", + " elseif CAT==90, !- Program Line 1404\n", + " if PMOT < ACSTall, !- Program Line 1405\n", + " set ACST = 24+ACSTtol, !- Program Line 1406\n", + " elseif PMOT > ACSTaul, !- Program Line 1407\n", + " set ACST = 26+ACSTtol, !- Program Line 1408\n", + " endif, !- Program Line 1409\n", + " endif, !- Program Line 1410\n", + " endif, !- Program Line 1411\n", + " if (ComfStand == 15) && (ComfMod == 2), !- Program Line 1412\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1413\n", + " set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1414\n", + " elseif CAT==80, !- Program Line 1415\n", + " if PMOT < AHSTall, !- Program Line 1416\n", + " set AHST = 19+AHSTtol, !- Program Line 1417\n", + " elseif PMOT > AHSTaul, !- Program Line 1418\n", + " set AHST = 22+AHSTtol, !- Program Line 1419\n", + " endif, !- Program Line 1420\n", + " elseif CAT==90, !- Program Line 1421\n", + " if PMOT < AHSTall, !- Program Line 1422\n", + " set AHST = 20+AHSTtol, !- Program Line 1423\n", + " elseif PMOT > AHSTaul, !- Program Line 1424\n", + " set AHST = 23+AHSTtol, !- Program Line 1425\n", + " endif, !- Program Line 1426\n", + " endif, !- Program Line 1427\n", + " endif, !- Program Line 1428\n", + " if (ComfStand == 15) && (ComfMod == 3), !- Program Line 1429\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1430\n", + " set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1431\n", + " elseif PMOT < ACSTall, !- Program Line 1432\n", + " set ACST = ACSTall*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1433\n", + " elseif PMOT > ACSTaul, !- Program Line 1434\n", + " set ACST = ACSTaul*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1435\n", + " endif, !- Program Line 1436\n", + " endif, !- Program Line 1437\n", + " if (ComfStand == 15) && (ComfMod == 3), !- Program Line 1438\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1439\n", + " set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1440\n", + " elseif PMOT < AHSTall, !- Program Line 1441\n", + " set AHST = AHSTall*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1442\n", + " elseif PMOT > AHSTaul, !- Program Line 1443\n", + " set AHST = AHSTaul*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1444\n", + " endif, !- Program Line 1445\n", + " endif, !- Program Line 1446\n", + " if (ComfStand == 16) && (ComfMod == 1), !- Program Line 1447\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1448\n", + " set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1449\n", + " elseif CAT==80, !- Program Line 1450\n", + " if PMOT < ACSTall, !- Program Line 1451\n", + " set ACST = 23.5+ACSTtol, !- Program Line 1452\n", + " elseif PMOT > ACSTaul, !- Program Line 1453\n", + " set ACST = 25.5+ACSTtol, !- Program Line 1454\n", + " endif, !- Program Line 1455\n", + " elseif CAT==90, !- Program Line 1456\n", + " if PMOT < ACSTall, !- Program Line 1457\n", + " set ACST = 23+ACSTtol, !- Program Line 1458\n", + " elseif PMOT > ACSTaul, !- Program Line 1459\n", + " set ACST = 25+ACSTtol, !- Program Line 1460\n", + " endif, !- Program Line 1461\n", + " endif, !- Program Line 1462\n", + " endif, !- Program Line 1463\n", + " if (ComfStand == 16) && (ComfMod == 1), !- Program Line 1464\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1465\n", + " set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1466\n", + " elseif CAT==80, !- Program Line 1467\n", + " if PMOT < AHSTall, !- Program Line 1468\n", + " set AHST = 21+AHSTtol, !- Program Line 1469\n", + " elseif PMOT > AHSTaul, !- Program Line 1470\n", + " set AHST = 22.5+AHSTtol, !- Program Line 1471\n", + " endif, !- Program Line 1472\n", + " elseif CAT==90, !- Program Line 1473\n", + " if PMOT < AHSTall, !- Program Line 1474\n", + " set AHST = 23+AHSTtol, !- Program Line 1475\n", + " elseif PMOT > AHSTaul, !- Program Line 1476\n", + " set AHST = 23+AHSTtol, !- Program Line 1477\n", + " endif, !- Program Line 1478\n", + " endif, !- Program Line 1479\n", + " endif, !- Program Line 1480\n", + " if (ComfStand == 16) && (ComfMod == 2), !- Program Line 1481\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1482\n", + " set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1483\n", + " elseif CAT==80, !- Program Line 1484\n", + " if PMOT < ACSTall, !- Program Line 1485\n", + " set ACST = 25+ACSTtol, !- Program Line 1486\n", + " elseif PMOT > ACSTaul, !- Program Line 1487\n", + " set ACST = 27+ACSTtol, !- Program Line 1488\n", + " endif, !- Program Line 1489\n", + " elseif CAT==90, !- Program Line 1490\n", + " if PMOT < ACSTall, !- Program Line 1491\n", + " set ACST = 24+ACSTtol, !- Program Line 1492\n", + " elseif PMOT > ACSTaul, !- Program Line 1493\n", + " set ACST = 26+ACSTtol, !- Program Line 1494\n", + " endif, !- Program Line 1495\n", + " endif, !- Program Line 1496\n", + " endif, !- Program Line 1497\n", + " if (ComfStand == 16) && (ComfMod == 2), !- Program Line 1498\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1499\n", + " set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1500\n", + " elseif CAT==80, !- Program Line 1501\n", + " if PMOT < AHSTall, !- Program Line 1502\n", + " set AHST = 19+AHSTtol, !- Program Line 1503\n", + " elseif PMOT > AHSTaul, !- Program Line 1504\n", + " set AHST = 22+AHSTtol, !- Program Line 1505\n", + " endif, !- Program Line 1506\n", + " elseif CAT==90, !- Program Line 1507\n", + " if PMOT < AHSTall, !- Program Line 1508\n", + " set AHST = 20+AHSTtol, !- Program Line 1509\n", + " elseif PMOT > AHSTaul, !- Program Line 1510\n", + " set AHST = 23+AHSTtol, !- Program Line 1511\n", + " endif, !- Program Line 1512\n", + " endif, !- Program Line 1513\n", + " endif, !- Program Line 1514\n", + " if (ComfStand == 16) && (ComfMod == 3), !- Program Line 1515\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1516\n", + " set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1517\n", + " elseif PMOT < ACSTall, !- Program Line 1518\n", + " set ACST = ACSTall*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1519\n", + " elseif PMOT > ACSTaul, !- Program Line 1520\n", + " set ACST = ACSTaul*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1521\n", + " endif, !- Program Line 1522\n", + " endif, !- Program Line 1523\n", + " if (ComfStand == 16) && (ComfMod == 3), !- Program Line 1524\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1525\n", + " set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1526\n", + " elseif PMOT < AHSTall, !- Program Line 1527\n", + " set AHST = AHSTall*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1528\n", + " elseif PMOT > AHSTaul, !- Program Line 1529\n", + " set AHST = AHSTaul*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1530\n", + " endif, !- Program Line 1531\n", + " endif, !- Program Line 1532\n", + " if (ComfStand == 17) || (ComfStand == 18) || (ComfStand == 19) || (ComfStand == 20), !- Program Line 1533\n", + " if ComfMod == 0, !- Program Line 1534\n", + " set ACST = 25+ACSTtol, !- Program Line 1535\n", + " set AHST = 20+AHSTtol, !- Program Line 1536\n", + " endif, !- Program Line 1537\n", + " endif, !- Program Line 1538\n", + " if (ComfStand == 17) && (ComfMod == 1), !- Program Line 1539\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1540\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1541\n", + " set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1542\n", + " else, !- Program Line 1543\n", + " set ACST = 25+ACSTtol, !- Program Line 1544\n", + " endif, !- Program Line 1545\n", + " else, !- Program Line 1546\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1547\n", + " set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1548\n", + " else, !- Program Line 1549\n", + " set ACST = 25+ACSTtol, !- Program Line 1550\n", + " endif, !- Program Line 1551\n", + " endif, !- Program Line 1552\n", + " endif, !- Program Line 1553\n", + " if (ComfStand == 17) && (ComfMod == 1), !- Program Line 1554\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1555\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1556\n", + " set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1557\n", + " else, !- Program Line 1558\n", + " set AHST = 20+AHSTtol, !- Program Line 1559\n", + " endif, !- Program Line 1560\n", + " else, !- Program Line 1561\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1562\n", + " set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1563\n", + " else, !- Program Line 1564\n", + " set AHST = 20+AHSTtol, !- Program Line 1565\n", + " endif, !- Program Line 1566\n", + " endif, !- Program Line 1567\n", + " endif, !- Program Line 1568\n", + " if (ComfStand == 17) && (ComfMod == 2), !- Program Line 1569\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1570\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1571\n", + " set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1572\n", + " elseif PMOT < ACSTall, !- Program Line 1573\n", + " if CAT == 90, !- Program Line 1574\n", + " set ACST = 24+ACSTtol, !- Program Line 1575\n", + " elseif CAT == 80, !- Program Line 1576\n", + " set ACST = 25+ACSTtol, !- Program Line 1577\n", + " endif, !- Program Line 1578\n", + " elseif PMOT > ACSTaul, !- Program Line 1579\n", + " if CAT == 90, !- Program Line 1580\n", + " set ACST = 26+ACSTtol, !- Program Line 1581\n", + " elseif CAT == 80, !- Program Line 1582\n", + " set ACST = 27+ACSTtol, !- Program Line 1583\n", + " endif, !- Program Line 1584\n", + " endif, !- Program Line 1585\n", + " else, !- Program Line 1586\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1587\n", + " set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1588\n", + " elseif PMOT < ACSTall, !- Program Line 1589\n", + " if CAT == 90, !- Program Line 1590\n", + " set ACST = 24+ACSTtol, !- Program Line 1591\n", + " elseif CAT == 80, !- Program Line 1592\n", + " set ACST = 25+ACSTtol, !- Program Line 1593\n", + " endif, !- Program Line 1594\n", + " elseif PMOT > ACSTaul, !- Program Line 1595\n", + " if CAT == 90, !- Program Line 1596\n", + " set ACST = 26+ACSTtol, !- Program Line 1597\n", + " elseif CAT == 80, !- Program Line 1598\n", + " set ACST = 27+ACSTtol, !- Program Line 1599\n", + " endif, !- Program Line 1600\n", + " endif, !- Program Line 1601\n", + " endif, !- Program Line 1602\n", + " endif, !- Program Line 1603\n", + " if (ComfStand == 17) && (ComfMod == 2), !- Program Line 1604\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1605\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1606\n", + " set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1607\n", + " elseif PMOT < AHSTall, !- Program Line 1608\n", + " if CAT == 90, !- Program Line 1609\n", + " set AHST = 20+AHSTtol, !- Program Line 1610\n", + " elseif CAT == 80, !- Program Line 1611\n", + " set AHST = 19+AHSTtol, !- Program Line 1612\n", + " endif, !- Program Line 1613\n", + " elseif PMOT > AHSTaul, !- Program Line 1614\n", + " if CAT == 90, !- Program Line 1615\n", + " set AHST = 23+AHSTtol, !- Program Line 1616\n", + " elseif CAT == 80, !- Program Line 1617\n", + " set AHST = 22+AHSTtol, !- Program Line 1618\n", + " endif, !- Program Line 1619\n", + " endif, !- Program Line 1620\n", + " else, !- Program Line 1621\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1622\n", + " set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1623\n", + " elseif PMOT < AHSTall, !- Program Line 1624\n", + " if CAT == 90, !- Program Line 1625\n", + " set AHST = 20+AHSTtol, !- Program Line 1626\n", + " elseif CAT == 80, !- Program Line 1627\n", + " set AHST = 19+AHSTtol, !- Program Line 1628\n", + " endif, !- Program Line 1629\n", + " elseif PMOT > AHSTaul, !- Program Line 1630\n", + " if CAT == 90, !- Program Line 1631\n", + " set AHST = 23+AHSTtol, !- Program Line 1632\n", + " elseif CAT == 80, !- Program Line 1633\n", + " set AHST = 22+AHSTtol, !- Program Line 1634\n", + " endif, !- Program Line 1635\n", + " endif, !- Program Line 1636\n", + " endif, !- Program Line 1637\n", + " endif, !- Program Line 1638\n", + " if (ComfStand == 17) && (ComfMod == 3), !- Program Line 1639\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1640\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1641\n", + " set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1642\n", + " elseif PMOT < ACSTall, !- Program Line 1643\n", + " set ACST = ACSTall*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1644\n", + " elseif PMOT > ACSTaul, !- Program Line 1645\n", + " set ACST = ACSTaul*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1646\n", + " endif, !- Program Line 1647\n", + " else, !- Program Line 1648\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1649\n", + " set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1650\n", + " elseif PMOT < ACSTall, !- Program Line 1651\n", + " set ACST = ACSTall*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1652\n", + " elseif PMOT > ACSTaul, !- Program Line 1653\n", + " set ACST = ACSTaul*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1654\n", + " endif, !- Program Line 1655\n", + " endif, !- Program Line 1656\n", + " endif, !- Program Line 1657\n", + " if (ComfStand == 17) && (ComfMod == 3), !- Program Line 1658\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1659\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1660\n", + " set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1661\n", + " elseif PMOT < AHSTall, !- Program Line 1662\n", + " set AHST = AHSTall*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1663\n", + " elseif PMOT > AHSTaul, !- Program Line 1664\n", + " set AHST = AHSTaul*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1665\n", + " endif, !- Program Line 1666\n", + " else, !- Program Line 1667\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1668\n", + " set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1669\n", + " elseif PMOT < AHSTall, !- Program Line 1670\n", + " set AHST = AHSTall*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1671\n", + " elseif PMOT > AHSTaul, !- Program Line 1672\n", + " set AHST = AHSTaul*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1673\n", + " endif, !- Program Line 1674\n", + " endif, !- Program Line 1675\n", + " endif, !- Program Line 1676\n", + " if (ComfStand == 18) && (ComfMod == 1), !- Program Line 1677\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1678\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1679\n", + " set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1680\n", + " else, !- Program Line 1681\n", + " set ACST = 25+ACSTtol, !- Program Line 1682\n", + " endif, !- Program Line 1683\n", + " else, !- Program Line 1684\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1685\n", + " set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1686\n", + " else, !- Program Line 1687\n", + " set ACST = 25+ACSTtol, !- Program Line 1688\n", + " endif, !- Program Line 1689\n", + " endif, !- Program Line 1690\n", + " endif, !- Program Line 1691\n", + " if (ComfStand == 18) && (ComfMod == 1), !- Program Line 1692\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1693\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1694\n", + " set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1695\n", + " else, !- Program Line 1696\n", + " set AHST = 20+AHSTtol, !- Program Line 1697\n", + " endif, !- Program Line 1698\n", + " else, !- Program Line 1699\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1700\n", + " set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1701\n", + " else, !- Program Line 1702\n", + " set AHST = 20+AHSTtol, !- Program Line 1703\n", + " endif, !- Program Line 1704\n", + " endif, !- Program Line 1705\n", + " endif, !- Program Line 1706\n", + " if (ComfStand == 18) && (ComfMod == 2), !- Program Line 1707\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1708\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1709\n", + " set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1710\n", + " elseif PMOT < ACSTall, !- Program Line 1711\n", + " if CAT == 90, !- Program Line 1712\n", + " set ACST = 24+ACSTtol, !- Program Line 1713\n", + " elseif CAT == 80, !- Program Line 1714\n", + " set ACST = 25+ACSTtol, !- Program Line 1715\n", + " endif, !- Program Line 1716\n", + " elseif PMOT > ACSTaul, !- Program Line 1717\n", + " if CAT == 90, !- Program Line 1718\n", + " set ACST = 26+ACSTtol, !- Program Line 1719\n", + " elseif CAT == 80, !- Program Line 1720\n", + " set ACST = 27+ACSTtol, !- Program Line 1721\n", + " endif, !- Program Line 1722\n", + " endif, !- Program Line 1723\n", + " else, !- Program Line 1724\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1725\n", + " set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1726\n", + " elseif PMOT < ACSTall, !- Program Line 1727\n", + " if CAT == 90, !- Program Line 1728\n", + " set ACST = 24+ACSTtol, !- Program Line 1729\n", + " elseif CAT == 80, !- Program Line 1730\n", + " set ACST = 25+ACSTtol, !- Program Line 1731\n", + " endif, !- Program Line 1732\n", + " elseif PMOT > ACSTaul, !- Program Line 1733\n", + " if CAT == 90, !- Program Line 1734\n", + " set ACST = 26+ACSTtol, !- Program Line 1735\n", + " elseif CAT == 80, !- Program Line 1736\n", + " set ACST = 27+ACSTtol, !- Program Line 1737\n", + " endif, !- Program Line 1738\n", + " endif, !- Program Line 1739\n", + " endif, !- Program Line 1740\n", + " endif, !- Program Line 1741\n", + " if (ComfStand == 18) && (ComfMod == 2), !- Program Line 1742\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1743\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1744\n", + " set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1745\n", + " elseif PMOT < AHSTall, !- Program Line 1746\n", + " if CAT == 90, !- Program Line 1747\n", + " set AHST = 20+AHSTtol, !- Program Line 1748\n", + " elseif CAT == 80, !- Program Line 1749\n", + " set AHST = 19+AHSTtol, !- Program Line 1750\n", + " endif, !- Program Line 1751\n", + " elseif PMOT > AHSTaul, !- Program Line 1752\n", + " if CAT == 90, !- Program Line 1753\n", + " set AHST = 23+AHSTtol, !- Program Line 1754\n", + " elseif CAT == 80, !- Program Line 1755\n", + " set AHST = 22+AHSTtol, !- Program Line 1756\n", + " endif, !- Program Line 1757\n", + " endif, !- Program Line 1758\n", + " else, !- Program Line 1759\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1760\n", + " set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1761\n", + " elseif PMOT < AHSTall, !- Program Line 1762\n", + " if CAT == 90, !- Program Line 1763\n", + " set AHST = 20+AHSTtol, !- Program Line 1764\n", + " elseif CAT == 80, !- Program Line 1765\n", + " set AHST = 19+AHSTtol, !- Program Line 1766\n", + " endif, !- Program Line 1767\n", + " elseif PMOT > AHSTaul, !- Program Line 1768\n", + " if CAT == 90, !- Program Line 1769\n", + " set AHST = 23+AHSTtol, !- Program Line 1770\n", + " elseif CAT == 80, !- Program Line 1771\n", + " set AHST = 22+AHSTtol, !- Program Line 1772\n", + " endif, !- Program Line 1773\n", + " endif, !- Program Line 1774\n", + " endif, !- Program Line 1775\n", + " endif, !- Program Line 1776\n", + " if (ComfStand == 18) && (ComfMod == 3), !- Program Line 1777\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1778\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1779\n", + " set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1780\n", + " elseif PMOT < ACSTall, !- Program Line 1781\n", + " set ACST = ACSTall*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1782\n", + " elseif PMOT > ACSTaul, !- Program Line 1783\n", + " set ACST = ACSTaul*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1784\n", + " endif, !- Program Line 1785\n", + " else, !- Program Line 1786\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1787\n", + " set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1788\n", + " elseif PMOT < ACSTall, !- Program Line 1789\n", + " set ACST = ACSTall*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1790\n", + " elseif PMOT > ACSTaul, !- Program Line 1791\n", + " set ACST = ACSTaul*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1792\n", + " endif, !- Program Line 1793\n", + " endif, !- Program Line 1794\n", + " endif, !- Program Line 1795\n", + " if (ComfStand == 18) && (ComfMod == 3), !- Program Line 1796\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1797\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1798\n", + " set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1799\n", + " elseif PMOT < AHSTall, !- Program Line 1800\n", + " set AHST = AHSTall*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1801\n", + " elseif PMOT > AHSTaul, !- Program Line 1802\n", + " set AHST = AHSTaul*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1803\n", + " endif, !- Program Line 1804\n", + " else, !- Program Line 1805\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1806\n", + " set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1807\n", + " elseif PMOT < AHSTall, !- Program Line 1808\n", + " set AHST = AHSTall*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1809\n", + " elseif PMOT > AHSTaul, !- Program Line 1810\n", + " set AHST = AHSTaul*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1811\n", + " endif, !- Program Line 1812\n", + " endif, !- Program Line 1813\n", + " endif, !- Program Line 1814\n", + " if (ComfStand == 19) && (ComfMod == 1), !- Program Line 1815\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1816\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1817\n", + " set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1818\n", + " else, !- Program Line 1819\n", + " set ACST = 25+ACSTtol, !- Program Line 1820\n", + " endif, !- Program Line 1821\n", + " else, !- Program Line 1822\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1823\n", + " set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1824\n", + " else, !- Program Line 1825\n", + " set ACST = 25+ACSTtol, !- Program Line 1826\n", + " endif, !- Program Line 1827\n", + " endif, !- Program Line 1828\n", + " endif, !- Program Line 1829\n", + " if (ComfStand == 19) && (ComfMod == 1), !- Program Line 1830\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1831\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1832\n", + " set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1833\n", + " else, !- Program Line 1834\n", + " set AHST = 20+AHSTtol, !- Program Line 1835\n", + " endif, !- Program Line 1836\n", + " else, !- Program Line 1837\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1838\n", + " set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1839\n", + " else, !- Program Line 1840\n", + " set AHST = 20+AHSTtol, !- Program Line 1841\n", + " endif, !- Program Line 1842\n", + " endif, !- Program Line 1843\n", + " endif, !- Program Line 1844\n", + " if (ComfStand == 19) && (ComfMod == 2), !- Program Line 1845\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1846\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1847\n", + " set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1848\n", + " elseif PMOT < ACSTall, !- Program Line 1849\n", + " if CAT == 90, !- Program Line 1850\n", + " set ACST = 24+ACSTtol, !- Program Line 1851\n", + " elseif CAT == 80, !- Program Line 1852\n", + " set ACST = 25+ACSTtol, !- Program Line 1853\n", + " endif, !- Program Line 1854\n", + " elseif PMOT > ACSTaul, !- Program Line 1855\n", + " if CAT == 90, !- Program Line 1856\n", + " set ACST = 26+ACSTtol, !- Program Line 1857\n", + " elseif CAT == 80, !- Program Line 1858\n", + " set ACST = 27+ACSTtol, !- Program Line 1859\n", + " endif, !- Program Line 1860\n", + " endif, !- Program Line 1861\n", + " else, !- Program Line 1862\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1863\n", + " set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1864\n", + " elseif PMOT < ACSTall, !- Program Line 1865\n", + " if CAT == 90, !- Program Line 1866\n", + " set ACST = 24+ACSTtol, !- Program Line 1867\n", + " elseif CAT == 80, !- Program Line 1868\n", + " set ACST = 25+ACSTtol, !- Program Line 1869\n", + " endif, !- Program Line 1870\n", + " elseif PMOT > ACSTaul, !- Program Line 1871\n", + " if CAT == 90, !- Program Line 1872\n", + " set ACST = 26+ACSTtol, !- Program Line 1873\n", + " elseif CAT == 80, !- Program Line 1874\n", + " set ACST = 27+ACSTtol, !- Program Line 1875\n", + " endif, !- Program Line 1876\n", + " endif, !- Program Line 1877\n", + " endif, !- Program Line 1878\n", + " endif, !- Program Line 1879\n", + " if (ComfStand == 19) && (ComfMod == 2), !- Program Line 1880\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1881\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1882\n", + " set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1883\n", + " elseif PMOT < AHSTall, !- Program Line 1884\n", + " if CAT == 90, !- Program Line 1885\n", + " set AHST = 20+AHSTtol, !- Program Line 1886\n", + " elseif CAT == 80, !- Program Line 1887\n", + " set AHST = 19+AHSTtol, !- Program Line 1888\n", + " endif, !- Program Line 1889\n", + " elseif PMOT > AHSTaul, !- Program Line 1890\n", + " if CAT == 90, !- Program Line 1891\n", + " set AHST = 23+AHSTtol, !- Program Line 1892\n", + " elseif CAT == 80, !- Program Line 1893\n", + " set AHST = 22+AHSTtol, !- Program Line 1894\n", + " endif, !- Program Line 1895\n", + " endif, !- Program Line 1896\n", + " else, !- Program Line 1897\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1898\n", + " set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1899\n", + " elseif PMOT < AHSTall, !- Program Line 1900\n", + " if CAT == 90, !- Program Line 1901\n", + " set AHST = 20+AHSTtol, !- Program Line 1902\n", + " elseif CAT == 80, !- Program Line 1903\n", + " set AHST = 19+AHSTtol, !- Program Line 1904\n", + " endif, !- Program Line 1905\n", + " elseif PMOT > AHSTaul, !- Program Line 1906\n", + " if CAT == 90, !- Program Line 1907\n", + " set AHST = 23+AHSTtol, !- Program Line 1908\n", + " elseif CAT == 80, !- Program Line 1909\n", + " set AHST = 22+AHSTtol, !- Program Line 1910\n", + " endif, !- Program Line 1911\n", + " endif, !- Program Line 1912\n", + " endif, !- Program Line 1913\n", + " endif, !- Program Line 1914\n", + " if (ComfStand == 19) && (ComfMod == 3), !- Program Line 1915\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1916\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1917\n", + " set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1918\n", + " elseif PMOT < ACSTall, !- Program Line 1919\n", + " set ACST = ACSTall*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1920\n", + " elseif PMOT > ACSTaul, !- Program Line 1921\n", + " set ACST = ACSTaul*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1922\n", + " endif, !- Program Line 1923\n", + " else, !- Program Line 1924\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1925\n", + " set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1926\n", + " elseif PMOT < ACSTall, !- Program Line 1927\n", + " set ACST = ACSTall*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1928\n", + " elseif PMOT > ACSTaul, !- Program Line 1929\n", + " set ACST = ACSTaul*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1930\n", + " endif, !- Program Line 1931\n", + " endif, !- Program Line 1932\n", + " endif, !- Program Line 1933\n", + " if (ComfStand == 19) && (ComfMod == 3), !- Program Line 1934\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1935\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1936\n", + " set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1937\n", + " elseif PMOT < AHSTall, !- Program Line 1938\n", + " set AHST = AHSTall*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1939\n", + " elseif PMOT > AHSTaul, !- Program Line 1940\n", + " set AHST = AHSTaul*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1941\n", + " endif, !- Program Line 1942\n", + " else, !- Program Line 1943\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1944\n", + " set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1945\n", + " elseif PMOT < AHSTall, !- Program Line 1946\n", + " set AHST = AHSTall*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1947\n", + " elseif PMOT > AHSTaul, !- Program Line 1948\n", + " set AHST = AHSTaul*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1949\n", + " endif, !- Program Line 1950\n", + " endif, !- Program Line 1951\n", + " endif, !- Program Line 1952\n", + " if (ComfStand == 20) && (ComfMod == 1), !- Program Line 1953\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1954\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1955\n", + " set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 1956\n", + " else, !- Program Line 1957\n", + " set ACST = 25+ACSTtol, !- Program Line 1958\n", + " endif, !- Program Line 1959\n", + " else, !- Program Line 1960\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1961\n", + " set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 1962\n", + " else, !- Program Line 1963\n", + " set ACST = 25+ACSTtol, !- Program Line 1964\n", + " endif, !- Program Line 1965\n", + " endif, !- Program Line 1966\n", + " endif, !- Program Line 1967\n", + " if (ComfStand == 20) && (ComfMod == 1), !- Program Line 1968\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1969\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1970\n", + " set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 1971\n", + " else, !- Program Line 1972\n", + " set AHST = 20+AHSTtol, !- Program Line 1973\n", + " endif, !- Program Line 1974\n", + " else, !- Program Line 1975\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1976\n", + " set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 1977\n", + " else, !- Program Line 1978\n", + " set AHST = 20+AHSTtol, !- Program Line 1979\n", + " endif, !- Program Line 1980\n", + " endif, !- Program Line 1981\n", + " endif, !- Program Line 1982\n", + " if (ComfStand == 20) && (ComfMod == 2), !- Program Line 1983\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1984\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1985\n", + " set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 1986\n", + " elseif PMOT < ACSTall, !- Program Line 1987\n", + " if CAT == 90, !- Program Line 1988\n", + " set ACST = 24+ACSTtol, !- Program Line 1989\n", + " elseif CAT == 80, !- Program Line 1990\n", + " set ACST = 25+ACSTtol, !- Program Line 1991\n", + " endif, !- Program Line 1992\n", + " elseif PMOT > ACSTaul, !- Program Line 1993\n", + " if CAT == 90, !- Program Line 1994\n", + " set ACST = 26+ACSTtol, !- Program Line 1995\n", + " elseif CAT == 80, !- Program Line 1996\n", + " set ACST = 27+ACSTtol, !- Program Line 1997\n", + " endif, !- Program Line 1998\n", + " endif, !- Program Line 1999\n", + " else, !- Program Line 2000\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2001\n", + " set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2002\n", + " elseif PMOT < ACSTall, !- Program Line 2003\n", + " if CAT == 90, !- Program Line 2004\n", + " set ACST = 24+ACSTtol, !- Program Line 2005\n", + " elseif CAT == 80, !- Program Line 2006\n", + " set ACST = 25+ACSTtol, !- Program Line 2007\n", + " endif, !- Program Line 2008\n", + " elseif PMOT > ACSTaul, !- Program Line 2009\n", + " if CAT == 90, !- Program Line 2010\n", + " set ACST = 26+ACSTtol, !- Program Line 2011\n", + " elseif CAT == 80, !- Program Line 2012\n", + " set ACST = 27+ACSTtol, !- Program Line 2013\n", + " endif, !- Program Line 2014\n", + " endif, !- Program Line 2015\n", + " endif, !- Program Line 2016\n", + " endif, !- Program Line 2017\n", + " if (ComfStand == 20) && (ComfMod == 2), !- Program Line 2018\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2019\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2020\n", + " set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2021\n", + " elseif PMOT < AHSTall, !- Program Line 2022\n", + " if CAT == 90, !- Program Line 2023\n", + " set AHST = 20+AHSTtol, !- Program Line 2024\n", + " elseif CAT == 80, !- Program Line 2025\n", + " set AHST = 19+AHSTtol, !- Program Line 2026\n", + " endif, !- Program Line 2027\n", + " elseif PMOT > AHSTaul, !- Program Line 2028\n", + " if CAT == 90, !- Program Line 2029\n", + " set AHST = 23+AHSTtol, !- Program Line 2030\n", + " elseif CAT == 80, !- Program Line 2031\n", + " set AHST = 22+AHSTtol, !- Program Line 2032\n", + " endif, !- Program Line 2033\n", + " endif, !- Program Line 2034\n", + " else, !- Program Line 2035\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2036\n", + " set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2037\n", + " elseif PMOT < AHSTall, !- Program Line 2038\n", + " if CAT == 90, !- Program Line 2039\n", + " set AHST = 20+AHSTtol, !- Program Line 2040\n", + " elseif CAT == 80, !- Program Line 2041\n", + " set AHST = 19+AHSTtol, !- Program Line 2042\n", + " endif, !- Program Line 2043\n", + " elseif PMOT > AHSTaul, !- Program Line 2044\n", + " if CAT == 90, !- Program Line 2045\n", + " set AHST = 23+AHSTtol, !- Program Line 2046\n", + " elseif CAT == 80, !- Program Line 2047\n", + " set AHST = 22+AHSTtol, !- Program Line 2048\n", + " endif, !- Program Line 2049\n", + " endif, !- Program Line 2050\n", + " endif, !- Program Line 2051\n", + " endif, !- Program Line 2052\n", + " if (ComfStand == 20) && (ComfMod == 3), !- Program Line 2053\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2054\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2055\n", + " set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2056\n", + " elseif PMOT < ACSTall, !- Program Line 2057\n", + " set ACST = ACSTall*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2058\n", + " elseif PMOT > ACSTaul, !- Program Line 2059\n", + " set ACST = ACSTaul*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2060\n", + " endif, !- Program Line 2061\n", + " else, !- Program Line 2062\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2063\n", + " set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2064\n", + " elseif PMOT < ACSTall, !- Program Line 2065\n", + " set ACST = ACSTall*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2066\n", + " elseif PMOT > ACSTaul, !- Program Line 2067\n", + " set ACST = ACSTaul*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2068\n", + " endif, !- Program Line 2069\n", + " endif, !- Program Line 2070\n", + " endif, !- Program Line 2071\n", + " if (ComfStand == 20) && (ComfMod == 3), !- Program Line 2072\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2073\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2074\n", + " set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2075\n", + " elseif PMOT < AHSTall, !- Program Line 2076\n", + " set AHST = AHSTall*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2077\n", + " elseif PMOT > AHSTaul, !- Program Line 2078\n", + " set AHST = AHSTaul*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2079\n", + " endif, !- Program Line 2080\n", + " else, !- Program Line 2081\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2082\n", + " set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2083\n", + " elseif PMOT < AHSTall, !- Program Line 2084\n", + " set AHST = AHSTall*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2085\n", + " elseif PMOT > AHSTaul, !- Program Line 2086\n", + " set AHST = AHSTaul*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2087\n", + " endif, !- Program Line 2088\n", + " endif, !- Program Line 2089\n", + " endif, !- Program Line 2090\n", + " if (ComfStand == 21) && (ComfMod == 2), !- Program Line 2091\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2092\n", + " set ACST = PMOT*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2093\n", + " elseif CAT==80, !- Program Line 2094\n", + " if PMOT < ACSTall, !- Program Line 2095\n", + " set ACST = 25+ACSTtol, !- Program Line 2096\n", + " elseif PMOT > ACSTaul, !- Program Line 2097\n", + " set ACST = 27+ACSTtol, !- Program Line 2098\n", + " endif, !- Program Line 2099\n", + " elseif CAT==90, !- Program Line 2100\n", + " if PMOT < ACSTall, !- Program Line 2101\n", + " set ACST = 24+ACSTtol, !- Program Line 2102\n", + " elseif PMOT > ACSTaul, !- Program Line 2103\n", + " set ACST = 26+ACSTtol, !- Program Line 2104\n", + " endif, !- Program Line 2105\n", + " endif, !- Program Line 2106\n", + " endif, !- Program Line 2107\n", + " if (ComfStand == 21) && (ComfMod == 2), !- Program Line 2108\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2109\n", + " set AHST = PMOT*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2110\n", + " elseif CAT==80, !- Program Line 2111\n", + " if PMOT < AHSTall, !- Program Line 2112\n", + " set AHST = 19+AHSTtol, !- Program Line 2113\n", + " elseif PMOT > AHSTaul, !- Program Line 2114\n", + " set AHST = 22+AHSTtol, !- Program Line 2115\n", + " endif, !- Program Line 2116\n", + " elseif CAT==90, !- Program Line 2117\n", + " if PMOT < AHSTall, !- Program Line 2118\n", + " set AHST = 20+AHSTtol, !- Program Line 2119\n", + " elseif PMOT > AHSTaul, !- Program Line 2120\n", + " set AHST = 23+AHSTtol, !- Program Line 2121\n", + " endif, !- Program Line 2122\n", + " endif, !- Program Line 2123\n", + " endif, !- Program Line 2124\n", + " if (ComfStand == 21) && (ComfMod == 3), !- Program Line 2125\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2126\n", + " set ACST = PMOT*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2127\n", + " elseif PMOT < ACSTall, !- Program Line 2128\n", + " set ACST = ACSTall*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2129\n", + " elseif PMOT > ACSTaul, !- Program Line 2130\n", + " set ACST = ACSTaul*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2131\n", + " endif, !- Program Line 2132\n", + " endif, !- Program Line 2133\n", + " if (ComfStand == 21) && (ComfMod == 3), !- Program Line 2134\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2135\n", + " set AHST = PMOT*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2136\n", + " elseif PMOT < AHSTall, !- Program Line 2137\n", + " set AHST = AHSTall*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2138\n", + " elseif PMOT > AHSTaul, !- Program Line 2139\n", + " set AHST = AHSTaul*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2140\n", + " endif, !- Program Line 2141\n", + " endif, !- Program Line 2142\n", + " if (ComfStand == 22), !- Program Line 2143\n", + " if CoolingSeason == 1, !- Program Line 2144\n", + " if (CAT==3), !- Program Line 2145\n", + " set ACST = 24.5+2.5+ACSTtol, !- Program Line 2146\n", + " elseif (CAT==2), !- Program Line 2147\n", + " set ACST = 24.5+1.5+ACSTtol, !- Program Line 2148\n", + " elseif (CAT==1), !- Program Line 2149\n", + " set ACST = 24.5+1+ACSTtol, !- Program Line 2150\n", + " endif, !- Program Line 2151\n", + " else, !- Program Line 2152\n", + " if (CAT==3), !- Program Line 2153\n", + " set ACST = 22+3+ACSTtol, !- Program Line 2154\n", + " elseif (CAT==2), !- Program Line 2155\n", + " set ACST = 22+2+ACSTtol, !- Program Line 2156\n", + " elseif (CAT==1), !- Program Line 2157\n", + " set ACST = 22+1+ACSTtol, !- Program Line 2158\n", + " endif, !- Program Line 2159\n", + " endif, !- Program Line 2160\n", + " endif, !- Program Line 2161\n", + " if (ComfStand == 22), !- Program Line 2162\n", + " if CoolingSeason == 1, !- Program Line 2163\n", + " if (CAT==3), !- Program Line 2164\n", + " set AHST = 24.5-2.5+AHSTtol, !- Program Line 2165\n", + " elseif (CAT==2), !- Program Line 2166\n", + " set AHST = 24.5-1.5+AHSTtol, !- Program Line 2167\n", + " elseif (CAT==1), !- Program Line 2168\n", + " set AHST = 24.5-1+AHSTtol, !- Program Line 2169\n", + " endif, !- Program Line 2170\n", + " else, !- Program Line 2171\n", + " if (CAT==3), !- Program Line 2172\n", + " set AHST = 22-3+AHSTtol, !- Program Line 2173\n", + " elseif (CAT==2), !- Program Line 2174\n", + " set AHST = 22-2+AHSTtol, !- Program Line 2175\n", + " elseif (CAT==1), !- Program Line 2176\n", + " set AHST = 22-1+AHSTtol, !- Program Line 2177\n", + " endif, !- Program Line 2178\n", + " endif, !- Program Line 2179\n", + " endif, !- Program Line 2180\n", + " set ACSTx2 = ACST*SetpointAcc, !- Program Line 2181\n", + " set AHSTx2 = AHST*SetpointAcc, !- Program Line 2182\n", + " set roundedACSTx2 = @Round ACSTx2, !- Program Line 2183\n", + " set roundedAHSTx2 = @Round AHSTx2, !- Program Line 2184\n", + " if roundedACSTx2 - ACSTx2 < 0, !- Program Line 2185\n", + " set ACSTroundedUp = 0, !- Program Line 2186\n", + " else, !- Program Line 2187\n", + " set ACSTroundedUp = 1, !- Program Line 2188\n", + " endif, !- Program Line 2189\n", + " if roundedAHSTx2 - AHSTx2 < 0, !- Program Line 2190\n", + " set AHSTroundedUp = 0, !- Program Line 2191\n", + " else, !- Program Line 2192\n", + " set AHSTroundedUp = 1, !- Program Line 2193\n", + " endif, !- Program Line 2194\n", + " if ACSTroundedUp == 0, !- Program Line 2195\n", + " set roundedACST = roundedACSTx2 / SetpointAcc, !- Program Line 2196\n", + " else, !- Program Line 2197\n", + " set roundedACST = (roundedACSTx2 - 1) / SetpointAcc, !- Program Line 2198\n", + " endif, !- Program Line 2199\n", + " if AHSTroundedUp == 0, !- Program Line 2200\n", + " set roundedAHST = (roundedAHSTx2 + 1) / SetpointAcc, !- Program Line 2201\n", + " else, !- Program Line 2202\n", + " set roundedAHST = roundedAHSTx2 / SetpointAcc, !- Program Line 2203\n", + " endif; !- Program Line 2204]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setast']\n" + ] + }, + { + "cell_type": "markdown", + "id": "b2e1e8a5-4af9-4d23-82b9-af45b49ba26d", + "metadata": {}, + "source": [ + "If you want to inspect the `Parameter` objects, you can see the internal variable `parameters_list`:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4b72a5b5-417f-410b-b30a-bf6550dbb364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_m', min=0.01, max=0.99)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_n', min=5, max=23)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASToffset', min=2, max=4)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTall', min=10, max=15)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTaul', min=30, max=35)])]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_list" + ] + }, + { + "cell_type": "markdown", + "id": "16b3b170-683d-49b3-9d6b-4d3772245647", + "metadata": {}, + "source": [ + "## Running the parametric simulation" + ] + }, + { + "cell_type": "markdown", + "id": "e36956e3-781b-4fe1-980f-ad9fbca21fba", + "metadata": {}, + "source": [ + "### Setting the problem" + ] + }, + { + "cell_type": "markdown", + "id": "60150ec0-8c63-4097-bd4f-c8263dc22d47", + "metadata": {}, + "source": [ + "First, let's set the problem. To do so, use the `set_problem()` method. In case of the parametric simulation you don't need to input any argument. However, in case of the optimisation, you must input the arguments `minimize_outputs`, `constraints` and `constraint_bounds`, similarly as you would do in the besos `EPProblem` class." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "cd2c3ebc-99ea-46a2-94cf-37b3ddd77630", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_problem()" + ] + }, + { + "cell_type": "markdown", + "id": "cc1eac32-9784-441f-98e3-0382053ba14d", + "metadata": {}, + "source": [ + "Again, you can inspect the `EPProblem` class instance in the internal variable `problem`:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a4f80f41-febc-47e0-b610-8c07893a5c20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_m', min=0.01, max=0.99)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_n', min=5, max=23)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASToffset', min=2, max=4)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTall', min=10, max=15)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTaul', min=30, max=35)])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.problem" + ] + }, + { + "cell_type": "markdown", + "id": "901b319a-2ef8-4c7c-9fea-7512b81807f0", + "metadata": {}, + "source": [ + "### Sampling the simulation runs" + ] + }, + { + "cell_type": "markdown", + "id": "cecbc846-7227-498a-b0f8-f8795269df25", + "metadata": {}, + "source": [ + "The way to inform besos of the variations and permutations it must carry out in the parametric analysis is by means of a DataFrame, which must contain a column per `Parameter`, in which values are specified. There are multiple ways to do this DataFrame. For instance, we could make a dataframe from scratch:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "30f78385-766d-47f2-b394-c922f8403b30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustAST_mCustAST_nCustAST_ASToffsetCustAST_ASTallCustAST_ASTaul
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" + ], + "text/plain": [ + " CustAST_m CustAST_n CustAST_ASToffset CustAST_ASTall CustAST_ASTaul\n", + "0 0.1 22 2.5 10 35\n", + "1 0.6 8 4.0 10 35" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "param_dict = {\n", + " 'CustAST_m': [0.1, 0.6], \n", + " 'CustAST_n': [22, 8], \n", + " 'CustAST_ASToffset': [2.5, 4],\n", + " 'CustAST_ASTall': [10, 10],\n", + " 'CustAST_ASTaul': [35, 35],\n", + "}\n", + "input_param_df = pd.DataFrame(data=param_dict)\n", + "input_param_df" + ] + }, + { + "cell_type": "markdown", + "id": "242f6e72-de2e-4fa7-8265-f9d154e198f9", + "metadata": {}, + "source": [ + "We could input that df, which would result in 2 simulations. But now, imagine we want to make all possible combinations from the values we just did from scratch. We could use the function `make_all_combinations(parameters_values_dict)`. The argument `parameters_values_dict` must be a dictionary in the format {'parameter name': list_of_values}, such as the previouly defined param_dict, so let's input it. Let's see the possibilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "0f6fcf93-64d2-4a8f-9740-0e329c1dccfd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CustAST_m CustAST_n CustAST_ASToffset CustAST_ASTall CustAST_ASTaul\n", + "0 0.1 22 2.5 10 35\n", + "1 0.1 22 2.5 10 35\n", + "2 0.1 22 2.5 10 35\n", + "3 0.1 22 2.5 10 35\n", + "4 0.1 22 4.0 10 35\n", + "5 0.1 22 4.0 10 35\n", + "6 0.1 22 4.0 10 35\n", + "7 0.1 22 4.0 10 35\n", + "8 0.1 8 2.5 10 35\n", + "9 0.1 8 2.5 10 35\n", + "10 0.1 8 2.5 10 35\n", + "11 0.1 8 2.5 10 35\n", + "12 0.1 8 4.0 10 35\n", + "13 0.1 8 4.0 10 35\n", + "14 0.1 8 4.0 10 35\n", + "15 0.1 8 4.0 10 35\n", + "16 0.6 22 2.5 10 35\n", + "17 0.6 22 2.5 10 35\n", + "18 0.6 22 2.5 10 35\n", + "19 0.6 22 2.5 10 35\n", + "20 0.6 22 4.0 10 35\n", + "21 0.6 22 4.0 10 35\n", + "22 0.6 22 4.0 10 35\n", + "23 0.6 22 4.0 10 35\n", + "24 0.6 8 2.5 10 35\n", + "25 0.6 8 2.5 10 35\n", + "26 0.6 8 2.5 10 35\n", + "27 0.6 8 2.5 10 35\n", + "28 0.6 8 4.0 10 35\n", + "29 0.6 8 4.0 10 35\n", + "30 0.6 8 4.0 10 35\n", + "31 0.6 8 4.0 10 35" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_combinations = make_all_combinations(param_dict)\n", + "all_combinations" + ] + }, + { + "cell_type": "markdown", + "id": "9a525ce0-8b59-48ca-a160-59ff4781a8bb", + "metadata": {}, + "source": [ + "Also, we can use the sampling functions from besos (`full_factorial` and `lhs`), which have been wrapped in the methods `sampling_full_factorial(level)` and `sampling_lhs(num_samples)`. After calling these, the samples are saved in the internal variable `parameters_values_df`. Let's see some examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e617c6d0-bf66-40e7-a9f2-caa9cead6dff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfMod
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" + ], + "text/plain": [ + " ComfStand CAT ComfMod\n", + "2 2 80 3\n", + "3 2 90 3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_values_df" + ] + }, + { + "cell_type": "markdown", + "id": "c0c976c6-eed1-4727-a274-975585466130", + "metadata": {}, + "source": [ + "### Running the simulations" + ] + }, + { + "cell_type": "markdown", + "id": "302f66b3-55ba-468b-91bb-d0c7a53f4387", + "metadata": {}, + "source": [ + "Now, we're ready to run the simulations, by means of the `run_parametric_simulation(epws, out_dir, df, processes)` method. After calling the method, the outputs (a DataFrame) is saved in the internal variable `outputs_param_simulation`. It is based on the use of the `EvaluatorEP` class, `df_apply` method. We want to run the parametric simulations with both Sydney and Seville climate files, therefore the filenames are input in a list in the `epws` argument. The simulation outputs will be saved in a directory named 'notebook_temp_dir'. The values for the parameters will be driven by the internal variable `parameters_values_df`, as input in the `df` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5e5a19ab-d2a3-44ac-9d2d-01672ac8db08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n" + ] + } + ], + "source": [ + "parametric.run_parametric_simulation(\n", + " epws=['Sydney.epw', 'Seville.epw'],\n", + " out_dir='notebook_temp_dir',\n", + " df=parametric.parameters_values_df,\n", + " #processes=6, # The number of CPUs to be used. Default is 2.\n", + " #keep_input=True, # To keep the input values of parameters, as entered in df argument. Default is True.\n", + " #keep_dirs=True # To keep the simulation outputs. Default is True.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8f768acb-e813-4d4d-91d7-7b96a98ecaad", + "metadata": {}, + "source": [ + "Let's take a look at the simulation results" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9461f205-acd1-4532-b691-9dd7d9778873", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexComfStandCATComfModHeating:ElectricityCooling:ElectricityElectricity:HVACAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time seriesoutput_direpw
0228032.593138e+091.754898e+102.735351e+10[27.325804398148147, 27.91096527777778, 27.910...[20.325804398148147, 20.910965277777777, 20.91...[23.771739074031462, 23.897364637263966, 23.75...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8...Sydney
1329033.639536e+093.621666e+104.728630e+10[26.325804398148147, 26.91096527777778, 26.910...[21.325804398148147, 21.910965277777777, 21.91...[23.66668667903001, 23.8347876559133, 23.82938...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH...Sydney
2228032.062257e+093.171532e+104.054615e+10[24.785096064814812, 24.86211527777778, 24.862...[17.785096064814812, 17.86211527777778, 17.862...[21.151988618198942, 21.208271993109328, 20.26...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR...Seville
3329032.297988e+094.516874e+105.445162e+10[23.785096064814812, 23.86211527777778, 23.862...[18.785096064814812, 18.86211527777778, 18.862...[22.631374958463137, 20.71719912972282, 20.492...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H...Seville
\n", + "
" + ], + "text/plain": [ + " index ComfStand CAT ComfMod Heating:Electricity Cooling:Electricity \\\n", + "0 2 2 80 3 2.593138e+09 1.754898e+10 \n", + "1 3 2 90 3 3.639536e+09 3.621666e+10 \n", + "2 2 2 80 3 2.062257e+09 3.171532e+10 \n", + "3 3 2 90 3 2.297988e+09 4.516874e+10 \n", + "\n", + " Electricity:HVAC \\\n", + "0 2.735351e+10 \n", + "1 4.728630e+10 \n", + "2 4.054615e+10 \n", + "3 5.445162e+10 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 [27.325804398148147, 27.91096527777778, 27.910... \n", + "1 [26.325804398148147, 26.91096527777778, 26.910... \n", + "2 [24.785096064814812, 24.86211527777778, 24.862... \n", + "3 [23.785096064814812, 23.86211527777778, 23.862... \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 [20.325804398148147, 20.910965277777777, 20.91... \n", + "1 [21.325804398148147, 21.910965277777777, 21.91... \n", + "2 [17.785096064814812, 17.86211527777778, 17.862... \n", + "3 [18.785096064814812, 18.86211527777778, 18.862... \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 [23.771739074031462, 23.897364637263966, 23.75... \n", + "1 [23.66668667903001, 23.8347876559133, 23.82938... \n", + "2 [21.151988618198942, 21.208271993109328, 20.26... \n", + "3 [22.631374958463137, 20.71719912972282, 20.492... \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \\\n", + "0 [21.325694444444437, 21.325694444444437, 21.32... \n", + "1 [21.325694444444437, 21.325694444444437, 21.32... \n", + "2 [11.490694444444445, 11.490694444444445, 11.49... \n", + "3 [11.490694444444445, 11.490694444444445, 11.49... \n", + "\n", + " output_dir epw \n", + "0 notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8... Sydney \n", + "1 notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH... Sydney \n", + "2 notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR... Seville \n", + "3 notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H... Seville " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.outputs_param_simulation" + ] + }, + { + "cell_type": "markdown", + "id": "efa37b3d-45f9-4bbd-b2c2-3f36b743ee0b", + "metadata": {}, + "source": [ + "We can see the columns are the following:" + ] + }, + { + "cell_type": "markdown", + "id": "907ca41e-e151-4e90-a37b-ed0b596e1852", + "metadata": {}, + "source": [ + "- the parameters, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c733f7a2-fd24-4b14-9ab1-98075d261401", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand', 'CAT', 'ComfMod']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.value_descriptors[0].name for i in parametric.parameters_list]" + ] + }, + { + "cell_type": "markdown", + "id": "c30f391f-08a0-488e-8dd3-67aa9189790c", + "metadata": {}, + "source": [ + "- the outputs, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "405d7408-599d-4493-a497-5bfe686db381", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + " 'Adaptive Cooling Setpoint Temperature_No Tolerance_time series',\n", + " 'Adaptive Heating Setpoint Temperature_No Tolerance_time series',\n", + " 'Zone Operative Temperature_time series',\n", + " 'Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.name for i in parametric.sim_outputs]" + ] + }, + { + "cell_type": "markdown", + "id": "cf172d66-6883-481c-9779-a3382616924e", + "metadata": {}, + "source": [ + "- the path to the output files for each simulation, in the column 'out_dir'\n", + "- the epw for each simulation, in the column 'epw'" + ] + }, + { + "cell_type": "markdown", + "id": "e2f6302e-8105-482b-a984-c4bc03ac8086", + "metadata": {}, + "source": [ + "### Visualising the results" + ] + }, + { + "cell_type": "markdown", + "id": "8de1cbd1-8cf0-41c4-b394-6cf8481f2edd", + "metadata": {}, + "source": [ + "#### Aggregated columns" + ] + }, + { + "cell_type": "markdown", + "id": "d7b2ad11-4f2b-46d0-8a59-3e7e842b2ae9", + "metadata": {}, + "source": [ + "At this point, if you have some knowledge of pandas and some package to plot the data (e.g. matplotlib or seaborn), you can carry out your own analysis and visualization. We're going to do some example below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3b08c431-5c10-47bc-b8d9-bbed174fb35c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(\n", + " data=parametric.outputs_param_simulation,\n", + " x='Heating:Electricity',\n", + " y='Cooling:Electricity',\n", + " hue='CAT',\n", + " style='epw'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "710213aa-c8d2-4565-b121-a0db80753b52", + "metadata": {}, + "source": [ + "#### Time series columns" + ] + }, + { + "cell_type": "markdown", + "id": "d66994c0-7514-456f-8d9c-2c3acce6e656", + "metadata": {}, + "source": [ + "If you requested some output to be reported in time series, you can get a dataframe containing the hourly values using the method `get_hourly_df()`. This dataframe is saved in the internal variable `outputs_param_simulation_hourly`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "74848927-55f4-4e27-ae75-bf66baf49ba7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfModepwhourdatetimeAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series
02803Sydney12024-01-01 01:00:0027.32580420.32580423.77173921.325694
12803Sydney22024-01-01 02:00:0027.91096520.91096523.89736521.325694
22803Sydney32024-01-01 03:00:0027.91096520.91096523.75949921.325694
32803Sydney42024-01-01 04:00:0027.91096520.91096523.81012121.325694
42803Sydney52024-01-01 05:00:0027.91096520.91096524.11513121.325694
.................................
350352903Seville87562024-12-30 20:00:0023.93343018.93343023.61795711.720741
350362903Seville87572024-12-30 21:00:0023.93343018.93343023.57101211.720741
350372903Seville87582024-12-30 22:00:0023.93343018.93343023.34048011.720741
350382903Seville87592024-12-30 23:00:0023.93343018.93343023.06050111.720741
350392903Seville87602024-12-31 00:00:0023.93343018.93343022.83835211.720741
\n", + "

35040 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " ComfStand CAT ComfMod epw hour datetime \\\n", + "0 2 80 3 Sydney 1 2024-01-01 01:00:00 \n", + "1 2 80 3 Sydney 2 2024-01-01 02:00:00 \n", + "2 2 80 3 Sydney 3 2024-01-01 03:00:00 \n", + "3 2 80 3 Sydney 4 2024-01-01 04:00:00 \n", + "4 2 80 3 Sydney 5 2024-01-01 05:00:00 \n", + "... ... ... ... ... ... ... \n", + "35035 2 90 3 Seville 8756 2024-12-30 20:00:00 \n", + "35036 2 90 3 Seville 8757 2024-12-30 21:00:00 \n", + "35037 2 90 3 Seville 8758 2024-12-30 22:00:00 \n", + "35038 2 90 3 Seville 8759 2024-12-30 23:00:00 \n", + "35039 2 90 3 Seville 8760 2024-12-31 00:00:00 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 27.325804 \n", + "1 27.910965 \n", + "2 27.910965 \n", + "3 27.910965 \n", + "4 27.910965 \n", + "... ... \n", + "35035 23.933430 \n", + "35036 23.933430 \n", + "35037 23.933430 \n", + "35038 23.933430 \n", + "35039 23.933430 \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 20.325804 \n", + "1 20.910965 \n", + "2 20.910965 \n", + "3 20.910965 \n", + "4 20.910965 \n", + "... ... \n", + "35035 18.933430 \n", + "35036 18.933430 \n", + "35037 18.933430 \n", + "35038 18.933430 \n", + "35039 18.933430 \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 23.771739 \n", + "1 23.897365 \n", + "2 23.759499 \n", + "3 23.810121 \n", + "4 24.115131 \n", + "... ... \n", + "35035 23.617957 \n", + "35036 23.571012 \n", + "35037 23.340480 \n", + "35038 23.060501 \n", + "35039 22.838352 \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \n", + "0 21.325694 \n", + "1 21.325694 \n", + "2 21.325694 \n", + "3 21.325694 \n", + "4 21.325694 \n", + "... ... \n", + "35035 11.720741 \n", + "35036 11.720741 \n", + "35037 11.720741 \n", + "35038 11.720741 \n", + "35039 11.720741 \n", + "\n", + "[35040 rows x 10 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.get_hourly_df()\n", + "parametric.outputs_param_simulation_hourly" + ] + }, + { + "cell_type": "markdown", + "id": "dbac2c53-1b49-43a5-b8c7-02877ec8b4aa", + "metadata": {}, + "source": [ + "Let's prepare the `outputs_param_simulation_hourly` df for plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "921324d1-f102-4bd3-b43e-340e435bdca0", + "metadata": {}, + "outputs": [], + "source": [ + "#Let's make a copy of the dataframe to not to modify the original one\n", + "df = parametric.outputs_param_simulation_hourly.copy()\n", + "\n", + "# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot:\n", + "rmot = [i for i in df.columns if 'Running Average' in i][0]\n", + "\n", + "#Let's remove the columns where value is the same for all rows\n", + "for c in df.columns:\n", + " if len(set(df[c])) == 1:\n", + " df = df.drop(columns=[c])\n", + "#Now let's remove the hour and datetime columns, since will\n", + "df = df.drop(columns=['hour'])\n", + "\n", + "# Now let's reshape the df for plotting purposes\n", + "df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4aa2a50-88ee-4c66-ab3a-48a26eaec9c1", + "metadata": {}, + "source": [ + "Firstly, we're going to plot the hourly temperatures to see the slope of the comfort model, and check all hours are within thermal comfort limits (since we applied adaptive setpoints)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2213ebd0-2f3c-4e74-bd06-117ec72c3108", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.scatterplot,\n", + " x=rmot,\n", + " y='value',\n", + " hue='variable',\n", + " s=1,\n", + " #alpha=0.5\n", + ")\n", + "g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "\n", + "for lh in g._legend.legend_handles:\n", + " lh.set_markersize(5)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "14dfbd82-2e4f-45b6-96ca-da72265e117c", + "metadata": {}, + "source": [ + "Now, we're going to plot time on x-axis and change the plot type to lineplot, to see the variarion of the indoor operative temperature throughout the year:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b673ca88-bbd8-4c89-989e-7cf7c613e90c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.lineplot,\n", + " x='datetime',\n", + " y='value',\n", + " hue='variable',\n", + ")\n", + "g.set_axis_labels('Time', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0b253ef-b8ee-402b-a741-b4b89181e589", + "metadata": {}, + "source": [ + "We're done with the example, so let's remove all new files, so that we can re-run it again." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "abefda96-72f4-4e8a-8d2a-5337647c480b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'available_outputs', 'notebook_temp_dir'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_files = [i for i in listdir()]\n", + "new_files = set(current_files) - set(original_files)\n", + "new_files" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f9dbc462-7f2b-40e2-b7b0-41e9243b3b59", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "for item in new_files:\n", + " item_path = os.path.join(os.getcwd(), item)\n", + " if os.path.isfile(item_path):\n", + " os.remove(item_path)\n", + " elif os.path.isdir(item_path):\n", + " shutil.rmtree(item_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/param_sim_accim_predefined_model-checkpoint.ipynb b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/param_sim_accim_predefined_model-checkpoint.ipynb new file mode 100644 index 0000000..cb2d444 --- /dev/null +++ b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/.ipynb_checkpoints/param_sim_accim_predefined_model-checkpoint.ipynb @@ -0,0 +1,3069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2cf45ee0-4144-4034-99a9-311270c74d30", + "metadata": {}, + "source": [ + "# Parametric simulation using accim predefined models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f71c6322-10f1-4fde-bc8d-358163b8619b", + "metadata": {}, + "outputs": [], + "source": [ + "#todo import qgrid to manually change output dfs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a8361222-b96d-4121-9560-1480e3ee4f44", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import accim\n", + "from accim.parametric_and_optimisation.objectives import return_time_series\n", + "from besos import eppy_funcs as ef\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "from accim.utils import print_available_outputs_mod\n", + "from accim.parametric_and_optimisation.main import OptimParamSimulation, get_rdd_file_as_df, get_mdd_file_as_df, parse_mtd_file\n", + "from os import listdir\n" + ] + }, + { + "cell_type": "markdown", + "id": "c699ceba-f765-47c5-b5fd-b0cee5522b3a", + "metadata": {}, + "source": [ + "Let's have a look at the files we currently have in the path:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "233674c3-f15d-4917-aa47-430ae91486d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.ipynb_checkpoints',\n", + " 'accim_predefined_model.ipynb',\n", + " 'Seville.epw',\n", + " 'Sydney.epw',\n", + " 'TestModel.idf',\n", + " '__init__.py']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "original_files = [i for i in listdir()]\n", + "original_files" + ] + }, + { + "cell_type": "markdown", + "id": "16e31d87-3a87-40c1-aeef-d6ea1171291a", + "metadata": {}, + "source": [ + "Firstly, the IDF must be read using besos's `get_building` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d6e13f6-7466-4e96-a559-8c771895c1ac", + "metadata": {}, + "outputs": [], + "source": [ + "building = ef.get_building('TestModel.idf')" + ] + }, + { + "cell_type": "markdown", + "id": "affcedd2-3206-4178-bb93-3ae151e04301", + "metadata": {}, + "source": [ + "For this analysis, we want to use the HVAC system in all hours of the year, so that temperature is always comfortable. Therefore, we are going to set the occupancy to always on by means of the function `accim.utils.set_occupancy_to_always`, in which we input the IDF class instance we read in the previous cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f5c2feb1-ca1e-4b8a-bc50-1f116cca9fa4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On 24/7 Schedule:Compact object was already in the model.\n", + "People Block1:Zone2 Number of People Schedule Name has been set to always occupied.\n", + "People Block1:Zone1 Number of People Schedule Name has been set to always occupied.\n" + ] + } + ], + "source": [ + "accim.utils.set_occupancy_to_always(idf_object=building)" + ] + }, + { + "cell_type": "markdown", + "id": "29827b0f-9086-4e26-873b-db2f7818a18a", + "metadata": {}, + "source": [ + "Now, let's start with the settings for the parametric analysis. First, let's instantiate the class `OptimParamSimulation`, and let's pass the IDF instance in the argument `building`. Argument `parameters_type` can take 3 different strings:\n", + "- \"accim predefined model\", in which models are those previously defined in accim (ComfStand=0 to ComfStand=22);\n", + "- \"accim custom model\", in which key parameters of the adaptive comfort model are defined in the relevant arguments;\n", + "- \"apmv setpoints\", in which setpoints are based on the aPMV (Adaptive Predicted Mean Vote) instead of the PMV index;\n", + "\n", + "In this case, we're going to use the 'accim predefined model' type, in which the models we can use are those already defined in accim." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2a5fa261-bfee-4b08-a8fe-c62f46a481eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------\n", + "Adaptive-Comfort-Control-Implemented Model (ACCIM) v0.7.5\n", + "--------------------------------------------------------\n", + "\n", + "This tool allows to apply adaptive setpoint temperatures. \n", + "For further information, please read the documentation: \n", + "https://accim.readthedocs.io/en/master/\n", + "For a visual understanding of the tool, please visit the following jupyter notebooks:\n", + "- Using addAccis() to apply adaptive setpoint temperatures\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/addAccis/using_addAccis.html\n", + "- Using rename_epw_files() to rename the EPWs for proper data analysis after simulation\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/rename_epw_files/using_rename_epw_files.html\n", + "- Using runEp() to directly run simulations with EnergyPlus\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/runEp/using_runEp.html\n", + "- Using the class Table() for data analysis\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/Table/using_Table.html\n", + "- Full example\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/full_example/full_example.html\n", + "\n", + "Starting with the process.\n", + "Basic input data:\n", + "ScriptType is: vrf_mm\n", + "Supply Air Temperature Input Method is: temperature difference\n", + "Output type is: standard\n", + "Output frequencies are: \n", + "['hourly']\n", + "EnergyPlus version is: 23.1\n", + "Temperature Control method is: temperature\n", + "\n", + "=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================\n", + "\n", + "Starting with file:\n", + "The occupied zones in the model are:\n", + "BLOCK1:ZONE2\n", + "BLOCK1:ZONE1\n", + "The windows and doors in the model are:\n", + "Block1_Zone2_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_4_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_5_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_2_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_5_0_0_0_0_0_Win\n", + "The zones in the model are:\n", + "BLOCK1_ZONE2\n", + "BLOCK1_ZONE1\n", + "The people objects in the model have been amended.\n", + "BLOCK1:ZONE2 Thermostat has been added\n", + "BLOCK1:ZONE1 Thermostat has been added\n", + "On Schedule already was in the model\n", + "TypOperativeTempControlSch Schedule already was in the model\n", + "All ZoneHVAC:IdealLoadsAirSystem Heating and Cooling availability schedules has been set to on\n", + "On 24/7 Schedule already was in the model\n", + "Control type schedule: Always 4 Schedule has been added\n", + "Relative humidity setpoint schedule: Always 50.00 Schedule has been added\n", + "Heating Fanger comfort setpoint: Always -0.5 Schedule has been added\n", + "Cooling Fanger comfort setpoint: Always 0.1 Schedule has been added\n", + "Zone CO2 setpoint: Always 900ppm Schedule has been added\n", + "Min CO2 concentration: Always 600ppm Schedule has been added\n", + "Generic contaminant setpoint: Always 0.5ppm Schedule has been added\n", + "Air distribution effectiveness (always 1) Schedule has been added\n", + "VRF Heating Cooling (Northern Hemisphere) Schedule has been added\n", + "DefaultFanEffRatioCurve Curve:Cubic Object has been added\n", + "VRFTUCoolCapFT Curve:Cubic Object has been added\n", + "VRFTUHeatCapFT Curve:Cubic Object has been added\n", + "VRFCoolCapFTBoundary Curve:Cubic Object has been added\n", + "VRFCoolEIRFTBoundary Curve:Cubic Object has been added\n", + "CoolingEIRLowPLR Curve:Cubic Object has been added\n", + "VRFHeatCapFTBoundary Curve:Cubic Object has been added\n", + "VRFHeatEIRFTBoundary Curve:Cubic Object has been added\n", + "HeatingEIRLowPLR Curve:Cubic Object has been added\n", + "DefaultFanPowerRatioCurve Curve:Exponent Object has been added\n", + "DXHtgCoilDefrostEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFTHi Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatCapFT Curve:Biquadratic Object has been added\n", + "VRFHeatCapFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFT Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFTHi Curve:Biquadratic Object has been added\n", + "CoolingLengthCorrectionFactor Curve:Biquadratic Object has been added\n", + "VRF Piping Correction Factor for Length in Heating Mode Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Energy Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Energy Modifier Curve:Biquadratic Object has been added\n", + "VRFACCoolCapFFF Curve:Quadratic Object has been added\n", + "CoolingEIRHiPLR Curve:Quadratic Object has been added\n", + "VRFCPLFFPLR Curve:Quadratic Object has been added\n", + "HeatingEIRHiPLR Curve:Quadratic Object has been added\n", + "CoolingCombRatio Curve:Linear Object has been added\n", + "HeatingCombRatio Curve:Linear Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Zone List Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Zone List Object has been added\n", + "BLOCK1:ZONE2 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE1 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE2 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE1 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE2 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE1 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE2 Nodelist Objects has been added\n", + "BLOCK1:ZONE1 Nodelist Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Fan:ConstantVolume Object has been added\n", + "BLOCK1:ZONE1 Fan:ConstantVolume Object has been added\n", + "Vent_SP_temp Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "Added - SetComfTemp Program\n", + "Added - CountHours_BLOCK1_ZONE2 Program\n", + "Added - CountHours_BLOCK1_ZONE1 Program\n", + "Added - SetAppLimits Program\n", + "Added - ApplyCAT Program\n", + "Added - SetAST Program\n", + "Added - SetASTnoTol Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program\n", + "Added - SetInputData Program\n", + "Added - SetVOFinputData Program\n", + "Added - SetVST Program\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - Comfort Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Output Variable\n", + "Added - z_test_ComfStand Output Variable\n", + "Added - z_test_ACSTaul Output Variable\n", + "Added - z_test_ACSTall Output Variable\n", + "Added - z_test_AHSTaul Output Variable\n", + "Added - z_test_AHSTall Output Variable\n", + "Added - z_test_CAT Output Variable\n", + "Added - z_test_ACSToffset Output Variable\n", + "Added - z_test_AHSToffset Output Variable\n", + "Added - z_test_ComfMod Output Variable\n", + "Added - z_test_ACSTtol Output Variable\n", + "Added - z_test_SetpointAcc Output Variable\n", + "Added - z_test_CustAST_m Output Variable\n", + "Added - z_test_CustAST_n Output Variable\n", + "Added - z_test_AHSTtol Output Variable\n", + "Added - Ventilation Setpoint Temperature Output Variable\n", + "Added - Minimum Outdoor Temperature for ventilation Output Variable\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Multiplier for Ventilation Opening Factor Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Output Variable\n", + "Global variables objects have been added\n", + "Internal variables objects have been added\n", + "Added - RMOT Sensor\n", + "Added - PMOT Sensor\n", + "Added - Occ_count_BLOCK1_ZONE2 Sensor\n", + "Added - Occ_count_BLOCK1_ZONE1 Sensor\n", + "Added - BLOCK1_ZONE2_OpT Sensor\n", + "Added - BLOCK1_ZONE2_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE2_OutT Sensor\n", + "Added - BLOCK1_ZONE1_OpT Sensor\n", + "Added - BLOCK1_ZONE1_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE1_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - OutT Sensor\n", + "Added - AHST_Act_BLOCK1_ZONE2 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE2 Actuator\n", + "Added - AHST_Act_BLOCK1_ZONE1 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE1 Actuator\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - BLOCK1_ZONE2_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE2_HeatCoil Sensor\n", + "Added - BLOCK1_ZONE1_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE1_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - SetComfTemp Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetAppLimits Program Calling Manager\n", + "Added - ApplyCAT Program Calling Manager\n", + "Added - SetAST Program Calling Manager\n", + "Added - SetASTnoTol Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetInputData Program Calling Manager\n", + "Added - SetVOFinputData Program Calling Manager\n", + "Added - SetVST Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - Comfort Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfStand Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CAT Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfMod Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_SetpointAcc Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_m Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_n Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Multiplier for Ventilation Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Cooling Coil Total Cooling Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Heating Coil Heating Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electric Demand Power Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electricity Demand Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Surface Venting Window or Door Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Drybulb Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Wind Speed Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Relative Humidity Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Cooling Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Heating Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - OutputControl:Files object\n", + "Added - Output:VariableDictionary object\n", + "\n", + "=======================END OF OUTPUT IDF FILE GENERATION PROCESS=======================\n", + "\n" + ] + } + ], + "source": [ + "parametric = OptimParamSimulation(\n", + " building=building,\n", + " parameters_type='accim predefined model',\n", + " #output_type='standard', #\n", + " #output_keep_existing=False, #\n", + " #output_freqs=['hourly'], #\n", + " #ScriptType='vrf_mm', #\n", + " #SupplyAirTempInputMethod='temperature difference', #\n", + " #debugging=True, #\n", + " #verbosemode=False #\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51eed0eb-7b3a-4428-8952-070a650acf61", + "metadata": {}, + "source": [ + "An initial and generic version of the Adaptive-Comfort-Control-Implementation Script (ACCIS) has been added to the idf instance `building`. For instance, you can take a look at the parameter values accis currently has:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad387b77-4998-4e51-979a-928fa52e551d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetInputData, !- Name\n", + " set ComfStand = 1, !- Program Line 1\n", + " set CAT = 1, !- Program Line 2\n", + " set ComfMod = 2, !- Program Line 3\n", + " set HVACmode = 2, !- Program Line 4\n", + " set VentCtrl = 0, !- Program Line 5\n", + " set VSToffset = 0, !- Program Line 6\n", + " set MinOToffset = 7, !- Program Line 7\n", + " set MaxWindSpeed = 6, !- Program Line 8\n", + " set ACSTtol = -0.25, !- Program Line 9\n", + " set AHSTtol = 0.25, !- Program Line 10\n", + " set CoolSeasonStart = 121, !- Program Line 11\n", + " set CoolSeasonEnd = 274; !- Program Line 12]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata']" + ] + }, + { + "cell_type": "markdown", + "id": "99605e82-4d01-4aee-835c-ab9c6c66faa4", + "metadata": {}, + "source": [ + "## Setting the outputs" + ] + }, + { + "cell_type": "markdown", + "id": "bc714539-79c3-46e9-9caa-44a21fc413cb", + "metadata": {}, + "source": [ + "### Outputs for the idf (i.e. the outputs for each simulation run)" + ] + }, + { + "cell_type": "markdown", + "id": "764b7626-0e63-4317-8bbf-28ac2fe68f68", + "metadata": {}, + "source": [ + "First of all, we are going to set the outputs of the simulations that are going to be performed. This is an important step, especially if you are going to run hundreds or thousands of simulations." + ] + }, + { + "cell_type": "markdown", + "id": "b2f1d34a-dc0a-4aac-b01f-a7425b95090f", + "metadata": {}, + "source": [ + "Let's take a look at the Output:Variable objects we currently have in the idf. The method `get_output_var_df_from_idf()` returns a pandas DataFrame which contains the information of the existing Output:Variable objects in the idf:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "12ee78ba-c60b-4ef1-a6fb-51086a8a53fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
0*Comfort TemperatureHourly
1*Adaptive Cooling Setpoint TemperatureHourly
2*Adaptive Heating Setpoint TemperatureHourly
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
...............
72*VRF Heat Pump Heating Electricity EnergyHourly
73BLOCK1_ZONE2 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
74BLOCK1_ZONE2 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
75BLOCK1_ZONE1 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
76BLOCK1_ZONE1 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
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77 rows × 4 columns

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" + ], + "text/plain": [ + " key_value \\\n", + "0 * \n", + "1 * \n", + "2 * \n", + "3 * \n", + "4 * \n", + ".. ... \n", + "72 * \n", + "73 BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil \n", + "74 BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil \n", + "75 BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil \n", + "76 BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil \n", + "\n", + " variable_name reporting_frequency \\\n", + "0 Comfort Temperature Hourly \n", + "1 Adaptive Cooling Setpoint Temperature Hourly \n", + "2 Adaptive Heating Setpoint Temperature Hourly \n", + "3 Adaptive Cooling Setpoint Temperature_No Toler... Hourly \n", + "4 Adaptive Heating Setpoint Temperature_No Toler... Hourly \n", + ".. ... ... \n", + "72 VRF Heat Pump Heating Electricity Energy Hourly \n", + "73 Cooling Coil Total Cooling Rate Hourly \n", + "74 Heating Coil Heating Rate Hourly \n", + "75 Cooling Coil Total Cooling Rate Hourly \n", + "76 Heating Coil Heating Rate Hourly \n", + "\n", + " schedule_name \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + ".. ... \n", + "72 \n", + "73 \n", + "74 \n", + "75 \n", + "76 \n", + "\n", + "[77 rows x 4 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = parametric.get_output_var_df_from_idf()\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "ab1e81e8-c2f0-40c0-b868-0cf7ddb9954f", + "metadata": {}, + "source": [ + "now, let's see the Output:Meter objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "94c9844b-aacf-41fa-b1d4-ac7b491aa14e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [key_name, frequency]\n", + "Index: []" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "093bdcab-a1aa-492a-8864-d2e4e1a0b72c", + "metadata": {}, + "source": [ + "In this case, we can see there is no Output:Meter. However, there is a large number of Output:Variable objects which might result in heavy simulation outputs. So, let's get rid of some of them. We can drop the rows we want, and then input the modified DataFrame in the method `set_output_var_df_to_idf(outputs_df)`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9ccc7b08-119c-4f4c-bb45-9aac8201fe7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
52*Zone Operative TemperatureHourly
54*Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "3 * Adaptive Cooling Setpoint Temperature_No Toler... \n", + "4 * Adaptive Heating Setpoint Temperature_No Toler... \n", + "52 * Zone Operative Temperature \n", + "54 * Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " reporting_frequency schedule_name \n", + "3 Hourly \n", + "4 Hourly \n", + "52 Hourly \n", + "54 Hourly " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = df_output_variables_idf[\n", + " (\n", + " df_output_variables_idf['variable_name'].str.contains('Setpoint Temperature_No Tolerance')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Operative Temperature')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')\n", + " )\n", + "]\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "643151b4-a20d-4d00-a1c9-5a78a56be916", + "metadata": {}, + "source": [ + "Let's keep only the Output:Variable objects we have filtered using the `set_output_var_df_to_idf(outputs_df)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8958bc66-f42d-4fc0-9622-f2b3ffb03852", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf)" + ] + }, + { + "cell_type": "markdown", + "id": "eacd9d6d-7fa1-4e6d-8bab-edbfa322140b", + "metadata": {}, + "source": [ + "We have removed all rows except the adaptive heating and cooling setpoints, the operative temperature and the running mean outdoor temperature. Next optional step is adding Output:Meter objects. We can do that using the method `set_output_met_objects_to_idf(output_meters)`, where `output_meters` is a list of Output:Meter key names." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "addf36d3-b7c2-4254-bec5-322850a464d1", + "metadata": {}, + "outputs": [], + "source": [ + "output_meters = [\n", + " 'Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + "]\n", + "parametric.set_output_met_objects_to_idf(output_meters=output_meters)" + ] + }, + { + "cell_type": "markdown", + "id": "fa66a111-e72e-4ebd-8110-8222191d6c5d", + "metadata": {}, + "source": [ + "Let's see Output:Meter objects we currently have after adding these:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "41878d11-6dcc-4158-9ec4-a74aa9d2dafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
0Heating:Electricityhourly
1Cooling:Electricityhourly
2Electricity:HVAChourly
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" + ], + "text/plain": [ + " key_name frequency\n", + "0 Heating:Electricity hourly\n", + "1 Cooling:Electricity hourly\n", + "2 Electricity:HVAC hourly" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "90fd8d9b-ecd8-4c79-9f37-775f2c2921ed", + "metadata": {}, + "source": [ + "### Outputs to be read and shown in the parametric simulation or optimisation" + ] + }, + { + "cell_type": "markdown", + "id": "bb18fd43-e07e-42f1-ac75-020bcbe96d9a", + "metadata": {}, + "source": [ + "To successfully run the parametric simulation or optimisation, it is advisable running a test simulation to know the outputs that each simulation will have. We can do that with the method `get_outputs_df_from_testsim()`, which returns a tuple containing 2 DataFrames containing respectively the Output:Meter and Output:Variable objects from the simulation. In this case, you won't find wildcards such as \"*\"." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ff6093e9-840d-4142-ad79-98d78f4f13d1", + "metadata": {}, + "outputs": [], + "source": [ + "df_output_meters_testsim, df_output_variables_testsim = parametric.get_outputs_df_from_testsim()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "61a88134-7997-42ce-88b0-ef4606cdfe61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
0Heating:ElectricityHourly
1Cooling:ElectricityHourly
2Electricity:HVACHourly
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key_valuevariable_namefrequency
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly
2BLOCK1:ZONE2Zone Operative TemperatureHourly
3BLOCK1:ZONE1Zone Operative TemperatureHourly
4PEOPLE BLOCK1:ZONE2Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "2 BLOCK1:ZONE2 Zone Operative Temperature \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "4 PEOPLE BLOCK1:ZONE2 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency \n", + "0 Hourly \n", + "1 Hourly \n", + "2 Hourly \n", + "3 Hourly \n", + "4 Hourly \n", + "5 Hourly " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_testsim" + ] + }, + { + "cell_type": "markdown", + "id": "b0ed4500-6054-4a00-ae63-54412bf70944", + "metadata": {}, + "source": [ + "We can get DataFrames from the .rdd and .mdd files generated from the test simulation using the functions `get_rdd_file_as_df()` and `get_mdd_file_as_df()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e28454a5-8de7-4551-bf9b-4db3fa8f124f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectkey_valuevariable_namefrequencyunits
0Output:Variable*Site Outdoor Air Drybulb Temperaturehourly!- Zone Average [C]
1Output:Variable*Site Outdoor Air Dewpoint Temperaturehourly!- Zone Average [C]
2Output:Variable*Site Outdoor Air Wetbulb Temperaturehourly!- Zone Average [C]
3Output:Variable*Site Outdoor Air Humidity Ratiohourly!- Zone Average [kgWater/kgDryAir]
4Output:Variable*Site Outdoor Air Relative Humidityhourly!- Zone Average [%]
..................
712Output:Variable*Zone Ventilation When Unoccupied Timehourly!- HVAC Sum [hr]
713Output:Variable*Facility Any Zone Ventilation Below Target Voz...hourly!- HVAC Sum [hr]
714Output:Variable*Facility All Zones Ventilation At Target Voz Timehourly!- HVAC Sum [hr]
715Output:Variable*Facility Any Zone Ventilation Above Target Voz...hourly!- HVAC Sum [hr]
716Output:Variable*Facility Any Zone Ventilation When Unoccupied ...hourly!- HVAC Sum [hr]
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717 rows × 5 columns

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" + ], + "text/plain": [ + " object key_value \\\n", + "0 Output:Variable * \n", + "1 Output:Variable * \n", + "2 Output:Variable * \n", + "3 Output:Variable * \n", + "4 Output:Variable * \n", + ".. ... ... \n", + "712 Output:Variable * \n", + "713 Output:Variable * \n", + "714 Output:Variable * \n", + "715 Output:Variable * \n", + "716 Output:Variable * \n", + "\n", + " variable_name frequency \\\n", + "0 Site Outdoor Air Drybulb Temperature hourly \n", + "1 Site Outdoor Air Dewpoint Temperature hourly \n", + "2 Site Outdoor Air Wetbulb Temperature hourly \n", + "3 Site Outdoor Air Humidity Ratio hourly \n", + "4 Site Outdoor Air Relative Humidity hourly \n", + ".. ... ... \n", + "712 Zone Ventilation When Unoccupied Time hourly \n", + "713 Facility Any Zone Ventilation Below Target Voz... hourly \n", + "714 Facility All Zones Ventilation At Target Voz Time hourly \n", + "715 Facility Any Zone Ventilation Above Target Voz... hourly \n", + "716 Facility Any Zone Ventilation When Unoccupied ... hourly \n", + "\n", + " units \n", + "0 !- Zone Average [C] \n", + "1 !- Zone Average [C] \n", + "2 !- Zone Average [C] \n", + "3 !- Zone Average [kgWater/kgDryAir] \n", + "4 !- Zone Average [%] \n", + ".. ... \n", + "712 !- HVAC Sum [hr] \n", + "713 !- HVAC Sum [hr] \n", + "714 !- HVAC Sum [hr] \n", + "715 !- HVAC Sum [hr] \n", + "716 !- HVAC Sum [hr] \n", + "\n", + "[717 rows x 5 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rdd = get_rdd_file_as_df()\n", + "df_rdd" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "58649f6e-f08c-49e5-823a-376ff26eb83a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectmeter_namefrequencyunits
0Output:MeterElectricity:Facilityhourly!- [J]
1Output:Meter:CumulativeElectricity:Facilityhourly!- [J]
2Output:MeterElectricity:Buildinghourly!- [J]
3Output:Meter:CumulativeElectricity:Buildinghourly!- [J]
4Output:MeterElectricity:Zone:BLOCK1:ZONE2hourly!- [J]
...............
157Output:Meter:CumulativeGeneral:HeatRecovery:EnergyTransferhourly!- [J]
158Output:MeterCarbon Equivalent:Facilityhourly!- [kg]
159Output:Meter:CumulativeCarbon Equivalent:Facilityhourly!- [kg]
160Output:MeterCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
161Output:Meter:CumulativeCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
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162 rows × 4 columns

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" + ], + "text/plain": [ + " object meter_name \\\n", + "0 Output:Meter Electricity:Facility \n", + "1 Output:Meter:Cumulative Electricity:Facility \n", + "2 Output:Meter Electricity:Building \n", + "3 Output:Meter:Cumulative Electricity:Building \n", + "4 Output:Meter Electricity:Zone:BLOCK1:ZONE2 \n", + ".. ... ... \n", + "157 Output:Meter:Cumulative General:HeatRecovery:EnergyTransfer \n", + "158 Output:Meter Carbon Equivalent:Facility \n", + "159 Output:Meter:Cumulative Carbon Equivalent:Facility \n", + "160 Output:Meter CarbonEquivalentEmissions:Carbon Equivalent \n", + "161 Output:Meter:Cumulative CarbonEquivalentEmissions:Carbon Equivalent \n", + "\n", + " frequency units \n", + "0 hourly !- [J] \n", + "1 hourly !- [J] \n", + "2 hourly !- [J] \n", + "3 hourly !- [J] \n", + "4 hourly !- [J] \n", + ".. ... ... \n", + "157 hourly !- [J] \n", + "158 hourly !- [kg] \n", + "159 hourly !- [kg] \n", + "160 hourly !- [kg] \n", + "161 hourly !- [kg] \n", + "\n", + "[162 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mdd = get_mdd_file_as_df()\n", + "df_mdd" + ] + }, + { + "cell_type": "markdown", + "id": "a84943b5-db09-4f19-95bb-f9583711d0a7", + "metadata": {}, + "source": [ + "Also, we can parse the .mtd files as a list using the function `parse_mtd_file()`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df4da1a7-fcb2-4c59-b790-1bc9e3b128f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'meter_id': '14',\n", + " 'description': 'BLOCK1:ZONE2 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']},\n", + " {'meter_id': '135',\n", + " 'description': 'BLOCK1:ZONE1 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']}]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mtd_list = parse_mtd_file()\n", + "mtd_list[0:2]" + ] + }, + { + "cell_type": "markdown", + "id": "76fa398d-15a0-4504-9f11-468bfd7c094f", + "metadata": {}, + "source": [ + "Therefore, we have 2 DataFrames, one for the Output:Meter and another for the Output:Variable objects. Next step is setting the outputs for the parametric simulation. To do so, we'll need to pass the DataFrames into the method `set_outputs_for_simulation(df_output_meter, df_output_variable)`. If you have some knowledge about the python package besos, you might think of these dataframes as if each row was a `MeterReader` or `VariableReader` instances respectively for the Output:Meter and Output:Variable dataframes, and the arguments in these were the specified in the columns. The `MeterReader` class takes the arguments `key_name`, `frequency`, `name` and `func`, while `VariableReader` class takes the arguments `key_value`, `variable_name`, `frequency`, `name` and `func`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b133883-a0be-42a9-bc04-0e6054337b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_name', 'frequency']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_meters_testsim.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c43b0496-1d36-4a0e-b2dc-9f77e41627ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_value', 'variable_name', 'frequency']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_variables_testsim.columns]" + ] + }, + { + "cell_type": "markdown", + "id": "a03bd526-8bdf-4cce-af62-73ac36e9d0c3", + "metadata": {}, + "source": [ + "If you take a look at the columns of the dataframes above, you can see the names are the arguments in the `MeterReader` and `VariableReader` classes, and only `name` and `func` are missing. That means, you can add these columns to input the `name` and `func` arguments as desired. In case of the Output:Meter dataframe, we won't add the `name` and `func` columns, which means the name will be the `key_name` and hourly results will be aggregated using the pd.Series.sum() function. However, in case of the Output:Variable dataframe, we will specify these: we want the hourly values rather than the aggregation, therefore we will pass the name bound to the function `return_time_series`, and we will add '_time series' as a suffix to the `variable_name` column. We will also remove the outputs for BLOCK1:ZONE2, which are the rows 2 and 4." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0e8fecdc-4625-4d95-a87d-67071884963b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namefrequencyfuncname
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x00000267CB9B...Adaptive Cooling Setpoint Temperature_No Toler...
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x00000267CB9B...Adaptive Heating Setpoint Temperature_No Toler...
3BLOCK1:ZONE1Zone Operative TemperatureHourly<function return_time_series at 0x00000267CB9B...Zone Operative Temperature_time series
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly<function return_time_series at 0x00000267CB9B...Zone Thermal Comfort ASHRAE 55 Adaptive Model ...
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency func \\\n", + "0 Hourly , key_name='Heating:Electricity'),\n", + " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", + " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", + " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", + " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.sim_outputs" + ] + }, + { + "cell_type": "markdown", + "id": "b5dd98bb-6b1d-4b3c-b762-3c9a0c015f63", + "metadata": {}, + "source": [ + "## Setting the parameters" + ] + }, + { + "cell_type": "markdown", + "id": "4804beed-52e5-432a-a999-685c222d668d", + "metadata": {}, + "source": [ + "At the top of the script, when you instantiated the class `OptimParamSimulation`, you already specified which type of parameters you were going to use. Now, the parameters we're about to set, must match the `parameters_type` argument. At this point, you may not know which parameters you can use, so you can call the method `get_available_parameters()`, which will return a list of available parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "004029fa-ff72-4f43-a9c3-e004757463e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand',\n", + " 'CAT',\n", + " 'CATcoolOffset',\n", + " 'CATheatOffset',\n", + " 'ComfMod',\n", + " 'SetpointAcc',\n", + " 'CoolSeasonStart',\n", + " 'CoolSeasonEnd',\n", + " 'HVACmode',\n", + " 'VentCtrl',\n", + " 'MaxTempDiffVOF',\n", + " 'MinTempDiffVOF',\n", + " 'MultiplierVOF',\n", + " 'VSToffset',\n", + " 'MinOToffset',\n", + " 'MaxWindSpeed',\n", + " 'ASTtol']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "available_parameters = parametric.get_available_parameters()\n", + "available_parameters" + ] + }, + { + "cell_type": "markdown", + "id": "45886126-30b8-4e53-b8c0-06135e6a3c38", + "metadata": {}, + "source": [ + "If you don't know what are these, please refer to the [documentation](https://accim.readthedocs.io/en/master/4_detailed%20use.html).\n", + "\n", + "Using the 'accim predefined model' type, the values must be a list of options, since the values for some arguments are used to select some specific comfort model, and have a categorical use rather than numeric. Now, let's set the parameters using the method `set_parameters(accis_params_dict, additional_params)`. In this method, we set the parameters related to accim using the argument `accis_params_dict`, which takes a dictionary following the pattern {'parameter name': [1, 2, 3, etc]}. We can also add some other parameters not related to accim in the argument `additional_params`, which takes a list of parameters as if these were input straight to the besos EPProblem class. If additional arguments are added, the descriptors must be `CategoryParameter`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b4eb43dd-879b-4175-ab56-e9c04ea342af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named ComfStand.The name used as an input (ComfStand) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CAT.The name used as an input (CAT) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named ComfMod.The name used as an input (ComfMod) will be discarded.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "accis_parameters = {\n", + " 'ComfStand': [1, 2],\n", + " 'CAT': [80, 90],\n", + " 'ComfMod': [3],\n", + "}\n", + "parametric.set_parameters(accis_params_dict=accis_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "b2e1e8a5-4af9-4d23-82b9-af45b49ba26d", + "metadata": {}, + "source": [ + "If you want to inspect the `Parameter` objects, you can see the internal variable `parameters_list`:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4b72a5b5-417f-410b-b30a-bf6550dbb364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_list" + ] + }, + { + "cell_type": "markdown", + "id": "16b3b170-683d-49b3-9d6b-4d3772245647", + "metadata": {}, + "source": [ + "## Running the parametric simulation" + ] + }, + { + "cell_type": "markdown", + "id": "e36956e3-781b-4fe1-980f-ad9fbca21fba", + "metadata": {}, + "source": [ + "### Setting the problem" + ] + }, + { + "cell_type": "markdown", + "id": "60150ec0-8c63-4097-bd4f-c8263dc22d47", + "metadata": {}, + "source": [ + "First, let's set the problem. To do so, use the `set_problem()` method. In case of the parametric simulation you don't need to input any argument. However, in case of the optimisation, you must input the arguments `minimize_outputs`, `constraints` and `constraint_bounds`, similarly as you would do in the besos `EPProblem` class." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "cd2c3ebc-99ea-46a2-94cf-37b3ddd77630", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_problem()" + ] + }, + { + "cell_type": "markdown", + "id": "cc1eac32-9784-441f-98e3-0382053ba14d", + "metadata": {}, + "source": [ + "Again, you can inspect the `EPProblem` class instance in the internal variable `problem`:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a4f80f41-febc-47e0-b610-8c07893a5c20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.problem" + ] + }, + { + "cell_type": "markdown", + "id": "901b319a-2ef8-4c7c-9fea-7512b81807f0", + "metadata": {}, + "source": [ + "### Sampling the simulation runs" + ] + }, + { + "cell_type": "markdown", + "id": "cecbc846-7227-498a-b0f8-f8795269df25", + "metadata": {}, + "source": [ + "The way to inform besos of the variations and permutations it must carry out in the parametric analysis is by means of a DataFrame, which must contain a column per `Parameter`, in which values are specified. There are multiple ways to do this DataFrame. For instance, we could make a dataframe from scratch:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "30f78385-766d-47f2-b394-c922f8403b30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ComfStand CAT ComfMod\n", + "0 2 80 3\n", + "1 2 90 3" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "param_dict = {'ComfStand': [2, 2], 'CAT': [80, 90], 'ComfMod': [3, 3]}\n", + "input_param_df = pd.DataFrame(data=param_dict)\n", + "input_param_df" + ] + }, + { + "cell_type": "markdown", + "id": "9a525ce0-8b59-48ca-a160-59ff4781a8bb", + "metadata": {}, + "source": [ + "Also, we can use the sampling functions from besos (`full_factorial` and `lhs`), although these are not available using the accim predefined models, since these are based on ranges of values instead of options. In this case, we could use the `sampling_full_set()` method, which will combine all the values we entered when we set the parameters and drop the invalid combinations (e.g. you cannot use CAT=1, 2 or 3 if you are using ComfStand=2, since these CAT values are only available for ComfStand=1)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "286da8cc-4ee2-41b3-9d3a-07ba98f708e0", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.sampling_full_set()" + ] + }, + { + "cell_type": "markdown", + "id": "4f55e72f-f2b7-4f8a-940e-af5d23e0aa72", + "metadata": {}, + "source": [ + "Now, you can see the resulting input parameter dataframe in the internal variable `parameters_values_df`:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "dfebdd41-ee54-4fcf-ab7e-12db7e9461fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ComfStand CAT ComfMod\n", + "2 2 80 3\n", + "3 2 90 3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_values_df" + ] + }, + { + "cell_type": "markdown", + "id": "c0c976c6-eed1-4727-a274-975585466130", + "metadata": {}, + "source": [ + "### Running the simulations" + ] + }, + { + "cell_type": "markdown", + "id": "302f66b3-55ba-468b-91bb-d0c7a53f4387", + "metadata": {}, + "source": [ + "Now, we're ready to run the simulations, by means of the `run_parametric_simulation(epws, out_dir, df, processes)` method. After calling the method, the outputs (a DataFrame) is saved in the internal variable `outputs_param_simulation`. It is based on the use of the `EvaluatorEP` class, `df_apply` method. We want to run the parametric simulations with both Sydney and Seville climate files, therefore the filenames are input in a list in the `epws` argument. The simulation outputs will be saved in a directory named 'notebook_temp_dir'. The values for the parameters will be driven by the internal variable `parameters_values_df`, as input in the `df` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5e5a19ab-d2a3-44ac-9d2d-01672ac8db08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n" + ] + } + ], + "source": [ + "parametric.run_parametric_simulation(\n", + " epws=['Sydney.epw', 'Seville.epw'],\n", + " out_dir='notebook_temp_dir',\n", + " df=parametric.parameters_values_df,\n", + " #processes=6, # The number of CPUs to be used. Default is 2.\n", + " #keep_input=True, # To keep the input values of parameters, as entered in df argument. Default is True.\n", + " #keep_dirs=True # To keep the simulation outputs. Default is True.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8f768acb-e813-4d4d-91d7-7b96a98ecaad", + "metadata": {}, + "source": [ + "Let's take a look at the simulation results" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9461f205-acd1-4532-b691-9dd7d9778873", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexComfStandCATComfModHeating:ElectricityCooling:ElectricityElectricity:HVACAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time seriesoutput_direpw
0228032.593138e+091.754898e+102.735351e+10[27.325804398148147, 27.91096527777778, 27.910...[20.325804398148147, 20.910965277777777, 20.91...[23.771739074031462, 23.897364637263966, 23.75...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8...Sydney
1329033.639536e+093.621666e+104.728630e+10[26.325804398148147, 26.91096527777778, 26.910...[21.325804398148147, 21.910965277777777, 21.91...[23.66668667903001, 23.8347876559133, 23.82938...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH...Sydney
2228032.062257e+093.171532e+104.054615e+10[24.785096064814812, 24.86211527777778, 24.862...[17.785096064814812, 17.86211527777778, 17.862...[21.151988618198942, 21.208271993109328, 20.26...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR...Seville
3329032.297988e+094.516874e+105.445162e+10[23.785096064814812, 23.86211527777778, 23.862...[18.785096064814812, 18.86211527777778, 18.862...[22.631374958463137, 20.71719912972282, 20.492...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H...Seville
\n", + "
" + ], + "text/plain": [ + " index ComfStand CAT ComfMod Heating:Electricity Cooling:Electricity \\\n", + "0 2 2 80 3 2.593138e+09 1.754898e+10 \n", + "1 3 2 90 3 3.639536e+09 3.621666e+10 \n", + "2 2 2 80 3 2.062257e+09 3.171532e+10 \n", + "3 3 2 90 3 2.297988e+09 4.516874e+10 \n", + "\n", + " Electricity:HVAC \\\n", + "0 2.735351e+10 \n", + "1 4.728630e+10 \n", + "2 4.054615e+10 \n", + "3 5.445162e+10 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 [27.325804398148147, 27.91096527777778, 27.910... \n", + "1 [26.325804398148147, 26.91096527777778, 26.910... \n", + "2 [24.785096064814812, 24.86211527777778, 24.862... \n", + "3 [23.785096064814812, 23.86211527777778, 23.862... \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 [20.325804398148147, 20.910965277777777, 20.91... \n", + "1 [21.325804398148147, 21.910965277777777, 21.91... \n", + "2 [17.785096064814812, 17.86211527777778, 17.862... \n", + "3 [18.785096064814812, 18.86211527777778, 18.862... \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 [23.771739074031462, 23.897364637263966, 23.75... \n", + "1 [23.66668667903001, 23.8347876559133, 23.82938... \n", + "2 [21.151988618198942, 21.208271993109328, 20.26... \n", + "3 [22.631374958463137, 20.71719912972282, 20.492... \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \\\n", + "0 [21.325694444444437, 21.325694444444437, 21.32... \n", + "1 [21.325694444444437, 21.325694444444437, 21.32... \n", + "2 [11.490694444444445, 11.490694444444445, 11.49... \n", + "3 [11.490694444444445, 11.490694444444445, 11.49... \n", + "\n", + " output_dir epw \n", + "0 notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8... Sydney \n", + "1 notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH... Sydney \n", + "2 notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR... Seville \n", + "3 notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H... Seville " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.outputs_param_simulation" + ] + }, + { + "cell_type": "markdown", + "id": "efa37b3d-45f9-4bbd-b2c2-3f36b743ee0b", + "metadata": {}, + "source": [ + "We can see the columns are the following:" + ] + }, + { + "cell_type": "markdown", + "id": "907ca41e-e151-4e90-a37b-ed0b596e1852", + "metadata": {}, + "source": [ + "- the parameters, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c733f7a2-fd24-4b14-9ab1-98075d261401", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand', 'CAT', 'ComfMod']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.value_descriptors[0].name for i in parametric.parameters_list]" + ] + }, + { + "cell_type": "markdown", + "id": "c30f391f-08a0-488e-8dd3-67aa9189790c", + "metadata": {}, + "source": [ + "- the outputs, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "405d7408-599d-4493-a497-5bfe686db381", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + " 'Adaptive Cooling Setpoint Temperature_No Tolerance_time series',\n", + " 'Adaptive Heating Setpoint Temperature_No Tolerance_time series',\n", + " 'Zone Operative Temperature_time series',\n", + " 'Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.name for i in parametric.sim_outputs]" + ] + }, + { + "cell_type": "markdown", + "id": "cf172d66-6883-481c-9779-a3382616924e", + "metadata": {}, + "source": [ + "- the path to the output files for each simulation, in the column 'out_dir'\n", + "- the epw for each simulation, in the column 'epw'" + ] + }, + { + "cell_type": "markdown", + "id": "e2f6302e-8105-482b-a984-c4bc03ac8086", + "metadata": {}, + "source": [ + "### Visualising the results" + ] + }, + { + "cell_type": "markdown", + "id": "8de1cbd1-8cf0-41c4-b394-6cf8481f2edd", + "metadata": {}, + "source": [ + "#### Aggregated columns" + ] + }, + { + "cell_type": "markdown", + "id": "d7b2ad11-4f2b-46d0-8a59-3e7e842b2ae9", + "metadata": {}, + "source": [ + "At this point, if you have some knowledge of pandas and some package to plot the data (e.g. matplotlib or seaborn), you can carry out your own analysis and visualization. We're going to do some example below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3b08c431-5c10-47bc-b8d9-bbed174fb35c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(\n", + " data=parametric.outputs_param_simulation,\n", + " x='Heating:Electricity',\n", + " y='Cooling:Electricity',\n", + " hue='CAT',\n", + " style='epw'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "710213aa-c8d2-4565-b121-a0db80753b52", + "metadata": {}, + "source": [ + "#### Time series columns" + ] + }, + { + "cell_type": "markdown", + "id": "d66994c0-7514-456f-8d9c-2c3acce6e656", + "metadata": {}, + "source": [ + "If you requested some output to be reported in time series, you can get a dataframe containing the hourly values using the method `get_hourly_df()`. This dataframe is saved in the internal variable `outputs_param_simulation_hourly`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "74848927-55f4-4e27-ae75-bf66baf49ba7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfModepwhourdatetimeAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series
02803Sydney12024-01-01 01:00:0027.32580420.32580423.77173921.325694
12803Sydney22024-01-01 02:00:0027.91096520.91096523.89736521.325694
22803Sydney32024-01-01 03:00:0027.91096520.91096523.75949921.325694
32803Sydney42024-01-01 04:00:0027.91096520.91096523.81012121.325694
42803Sydney52024-01-01 05:00:0027.91096520.91096524.11513121.325694
.................................
350352903Seville87562024-12-30 20:00:0023.93343018.93343023.61795711.720741
350362903Seville87572024-12-30 21:00:0023.93343018.93343023.57101211.720741
350372903Seville87582024-12-30 22:00:0023.93343018.93343023.34048011.720741
350382903Seville87592024-12-30 23:00:0023.93343018.93343023.06050111.720741
350392903Seville87602024-12-31 00:00:0023.93343018.93343022.83835211.720741
\n", + "

35040 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " ComfStand CAT ComfMod epw hour datetime \\\n", + "0 2 80 3 Sydney 1 2024-01-01 01:00:00 \n", + "1 2 80 3 Sydney 2 2024-01-01 02:00:00 \n", + "2 2 80 3 Sydney 3 2024-01-01 03:00:00 \n", + "3 2 80 3 Sydney 4 2024-01-01 04:00:00 \n", + "4 2 80 3 Sydney 5 2024-01-01 05:00:00 \n", + "... ... ... ... ... ... ... \n", + "35035 2 90 3 Seville 8756 2024-12-30 20:00:00 \n", + "35036 2 90 3 Seville 8757 2024-12-30 21:00:00 \n", + "35037 2 90 3 Seville 8758 2024-12-30 22:00:00 \n", + "35038 2 90 3 Seville 8759 2024-12-30 23:00:00 \n", + "35039 2 90 3 Seville 8760 2024-12-31 00:00:00 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 27.325804 \n", + "1 27.910965 \n", + "2 27.910965 \n", + "3 27.910965 \n", + "4 27.910965 \n", + "... ... \n", + "35035 23.933430 \n", + "35036 23.933430 \n", + "35037 23.933430 \n", + "35038 23.933430 \n", + "35039 23.933430 \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 20.325804 \n", + "1 20.910965 \n", + "2 20.910965 \n", + "3 20.910965 \n", + "4 20.910965 \n", + "... ... \n", + "35035 18.933430 \n", + "35036 18.933430 \n", + "35037 18.933430 \n", + "35038 18.933430 \n", + "35039 18.933430 \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 23.771739 \n", + "1 23.897365 \n", + "2 23.759499 \n", + "3 23.810121 \n", + "4 24.115131 \n", + "... ... \n", + "35035 23.617957 \n", + "35036 23.571012 \n", + "35037 23.340480 \n", + "35038 23.060501 \n", + "35039 22.838352 \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \n", + "0 21.325694 \n", + "1 21.325694 \n", + "2 21.325694 \n", + "3 21.325694 \n", + "4 21.325694 \n", + "... ... \n", + "35035 11.720741 \n", + "35036 11.720741 \n", + "35037 11.720741 \n", + "35038 11.720741 \n", + "35039 11.720741 \n", + "\n", + "[35040 rows x 10 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.get_hourly_df()\n", + "parametric.outputs_param_simulation_hourly" + ] + }, + { + "cell_type": "markdown", + "id": "dbac2c53-1b49-43a5-b8c7-02877ec8b4aa", + "metadata": {}, + "source": [ + "Let's prepare the `outputs_param_simulation_hourly` df for plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "921324d1-f102-4bd3-b43e-340e435bdca0", + "metadata": {}, + "outputs": [], + "source": [ + "#Let's make a copy of the dataframe to not to modify the original one\n", + "df = parametric.outputs_param_simulation_hourly.copy()\n", + "\n", + "# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot:\n", + "rmot = [i for i in df.columns if 'Running Average' in i][0]\n", + "\n", + "#Let's remove the columns where value is the same for all rows\n", + "for c in df.columns:\n", + " if len(set(df[c])) == 1:\n", + " df = df.drop(columns=[c])\n", + "#Now let's remove the hour and datetime columns, since will\n", + "df = df.drop(columns=['hour'])\n", + "\n", + "# Now let's reshape the df for plotting purposes\n", + "df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4aa2a50-88ee-4c66-ab3a-48a26eaec9c1", + "metadata": {}, + "source": [ + "Firstly, we're going to plot the hourly temperatures to see the slope of the comfort model, and check all hours are within thermal comfort limits (since we applied adaptive setpoints)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2213ebd0-2f3c-4e74-bd06-117ec72c3108", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.scatterplot,\n", + " x=rmot,\n", + " y='value',\n", + " hue='variable',\n", + " s=1,\n", + " #alpha=0.5\n", + ")\n", + "g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "\n", + "for lh in g._legend.legend_handles:\n", + " lh.set_markersize(5)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "14dfbd82-2e4f-45b6-96ca-da72265e117c", + "metadata": {}, + "source": [ + "Now, we're going to plot time on x-axis and change the plot type to lineplot, to see the variarion of the indoor operative temperature throughout the year:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b673ca88-bbd8-4c89-989e-7cf7c613e90c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.lineplot,\n", + " x='datetime',\n", + " y='value',\n", + " hue='variable',\n", + ")\n", + "g.set_axis_labels('Time', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0b253ef-b8ee-402b-a741-b4b89181e589", + "metadata": {}, + "source": [ + "We're done with the example, so let's remove all new files, so that we can re-run it again." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "abefda96-72f4-4e8a-8d2a-5337647c480b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'available_outputs', 'notebook_temp_dir'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_files = [i for i in listdir()]\n", + "new_files = set(current_files) - set(original_files)\n", + "new_files" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f9dbc462-7f2b-40e2-b7b0-41e9243b3b59", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "for item in new_files:\n", + " item_path = os.path.join(os.getcwd(), item)\n", + " if os.path.isfile(item_path):\n", + " os.remove(item_path)\n", + " elif os.path.isdir(item_path):\n", + " shutil.rmtree(item_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/accim_predefined_model.ipynb b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/accim_predefined_model.ipynb deleted file mode 100644 index bc7833a..0000000 --- a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/accim_predefined_model.ipynb +++ /dev/null @@ -1,2274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2cf45ee0-4144-4034-99a9-311270c74d30", - "metadata": {}, - "source": [ - "# Parametric simulation using accim predefined models" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f71c6322-10f1-4fde-bc8d-358163b8619b", - "metadata": {}, - "outputs": [], - "source": [ - "#todo import qgrid to manually change output dfs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a8361222-b96d-4121-9560-1480e3ee4f44", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import accim\n", - "from accim.parametric_and_optimisation.objectives import return_time_series\n", - "from besos import eppy_funcs as ef\n", - "from matplotlib import pyplot as plt\n", - "from accim.utils import print_available_outputs_mod\n", - "from accim.parametric_and_optimisation.main import OptimParamSimulation, get_rdd_file_as_df, get_mdd_file_as_df, parse_mtd_file\n", - "from os import listdir\n" - ] - }, - { - "cell_type": "markdown", - "id": "c699ceba-f765-47c5-b5fd-b0cee5522b3a", - "metadata": {}, - "source": [ - "Let's have a look at the files we currently have in the path:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "233674c3-f15d-4917-aa47-430ae91486d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['.ipynb_checkpoints',\n", - " 'accim_predefined_model.ipynb',\n", - " 'Seville.epw',\n", - " 'Sydney.epw',\n", - " 'TestModel.idf',\n", - " '__init__.py']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "original_files = [i for i in listdir()]\n", - "original_files" - ] - }, - { - "cell_type": "markdown", - "id": "16e31d87-3a87-40c1-aeef-d6ea1171291a", - "metadata": {}, - "source": [ - "Firstly, the IDF must be read using besos's `get_building` function." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7d6e13f6-7466-4e96-a559-8c771895c1ac", - "metadata": {}, - "outputs": [], - "source": [ - "building = ef.get_building('TestModel.idf')" - ] - }, - { - "cell_type": "markdown", - "id": "affcedd2-3206-4178-bb93-3ae151e04301", - "metadata": {}, - "source": [ - "For this analysis, we want to use the HVAC system in all hours of the year, so that temperature is always comfortable. Therefore, we are going to set the occupancy to always on by means of the function `accim.utils.set_occupancy_to_always`, in which we input the IDF class instance we read in the previous cell." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f5c2feb1-ca1e-4b8a-bc50-1f116cca9fa4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "On 24/7 Schedule:Compact object was already in the model.\n", - "People Block1:Zone2 Number of People Schedule Name has been set to always occupied.\n", - "People Block1:Zone1 Number of People Schedule Name has been set to always occupied.\n" - ] - } - ], - "source": [ - "accim.utils.set_occupancy_to_always(idf_object=building)" - ] - }, - { - "cell_type": "markdown", - "id": "29827b0f-9086-4e26-873b-db2f7818a18a", - "metadata": {}, - "source": [ - "Now, let's start with the settings for the parametric analysis. First, let's instantiate the class `OptimParamSimulation`, and let's pass the IDF instance in the argument `building`. Argument `parameters_type` can take 3 different strings:\n", - "- \"accim predefined model\", in which models are those previously defined in accim (ComfStand=0 to ComfStand=22);\n", - "- \"accim custom model\", in which key parameters of the adaptive comfort model are defined in the relevant arguments;\n", - "- \"apmv setpoints\", in which setpoints are based on the aPMV (Adaptive Predicted Mean Vote) instead of the PMV index;\n", - "\n", - "In this case, we're going to use the 'accim predefined model' type, in which the models we can use are those already defined in accim." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2a5fa261-bfee-4b08-a8fe-c62f46a481eb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--------------------------------------------------------\n", - "Adaptive-Comfort-Control-Implemented Model (ACCIM) v0.7.5\n", - "--------------------------------------------------------\n", - "\n", - "This tool allows to apply adaptive setpoint temperatures. \n", - "For further information, please read the documentation: \n", - "https://accim.readthedocs.io/en/master/\n", - "For a visual understanding of the tool, please visit the following jupyter notebooks:\n", - "- Using addAccis() to apply adaptive setpoint temperatures\n", - "https://accim.readthedocs.io/en/master/jupyter_notebooks/addAccis/using_addAccis.html\n", - "- Using rename_epw_files() to rename the EPWs for proper data analysis after simulation\n", - "https://accim.readthedocs.io/en/master/jupyter_notebooks/rename_epw_files/using_rename_epw_files.html\n", - "- Using runEp() to directly run simulations with EnergyPlus\n", - "https://accim.readthedocs.io/en/master/jupyter_notebooks/runEp/using_runEp.html\n", - "- Using the class Table() for data analysis\n", - "https://accim.readthedocs.io/en/master/jupyter_notebooks/Table/using_Table.html\n", - "- Full example\n", - "https://accim.readthedocs.io/en/master/jupyter_notebooks/full_example/full_example.html\n", - "\n", - "Starting with the process.\n", - "Basic input data:\n", - "ScriptType is: vrf_mm\n", - "Supply Air Temperature Input Method is: temperature difference\n", - "Output type is: standard\n", - "Output frequencies are: \n", - "['hourly']\n", - "EnergyPlus version is: 23.1\n", - "Temperature Control method is: temperature\n", - "\n", - "=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================\n", - "\n", - "Starting with file:\n", - "The occupied zones in the model are:\n", - "BLOCK1:ZONE2\n", - "BLOCK1:ZONE1\n", - "The windows and doors in the model are:\n", - "Block1_Zone2_Wall_3_0_0_0_0_0_Win\n", - "Block1_Zone2_Wall_4_0_0_0_0_0_Win\n", - "Block1_Zone2_Wall_5_0_0_0_0_0_Win\n", - "Block1_Zone1_Wall_2_0_0_0_0_0_Win\n", - "Block1_Zone1_Wall_3_0_0_0_0_0_Win\n", - "Block1_Zone1_Wall_5_0_0_0_0_0_Win\n", - "The zones in the model are:\n", - "BLOCK1_ZONE2\n", - "BLOCK1_ZONE1\n", - "The people objects in the model have been amended.\n", - "BLOCK1:ZONE2 Thermostat has been added\n", - "BLOCK1:ZONE1 Thermostat has been added\n", - "On Schedule already was in the model\n", - "TypOperativeTempControlSch Schedule already was in the model\n", - "All ZoneHVAC:IdealLoadsAirSystem Heating and Cooling availability schedules has been set to on\n", - "On 24/7 Schedule already was in the model\n", - "Control type schedule: Always 4 Schedule has been added\n", - "Relative humidity setpoint schedule: Always 50.00 Schedule has been added\n", - "Heating Fanger comfort setpoint: Always -0.5 Schedule has been added\n", - "Cooling Fanger comfort setpoint: Always 0.1 Schedule has been added\n", - "Zone CO2 setpoint: Always 900ppm Schedule has been added\n", - "Min CO2 concentration: Always 600ppm Schedule has been added\n", - "Generic contaminant setpoint: Always 0.5ppm Schedule has been added\n", - "Air distribution effectiveness (always 1) Schedule has been added\n", - "VRF Heating Cooling (Northern Hemisphere) Schedule has been added\n", - "DefaultFanEffRatioCurve Curve:Cubic Object has been added\n", - "VRFTUCoolCapFT Curve:Cubic Object has been added\n", - "VRFTUHeatCapFT Curve:Cubic Object has been added\n", - "VRFCoolCapFTBoundary Curve:Cubic Object has been added\n", - "VRFCoolEIRFTBoundary Curve:Cubic Object has been added\n", - "CoolingEIRLowPLR Curve:Cubic Object has been added\n", - "VRFHeatCapFTBoundary Curve:Cubic Object has been added\n", - "VRFHeatEIRFTBoundary Curve:Cubic Object has been added\n", - "HeatingEIRLowPLR Curve:Cubic Object has been added\n", - "DefaultFanPowerRatioCurve Curve:Exponent Object has been added\n", - "DXHtgCoilDefrostEIRFT Curve:Biquadratic Object has been added\n", - "VRFCoolCapFT Curve:Biquadratic Object has been added\n", - "VRFCoolCapFTHi Curve:Biquadratic Object has been added\n", - "VRFCoolEIRFT Curve:Biquadratic Object has been added\n", - "VRFCoolEIRFTHi Curve:Biquadratic Object has been added\n", - "VRFHeatCapFT Curve:Biquadratic Object has been added\n", - "VRFHeatCapFTHi Curve:Biquadratic Object has been added\n", - "VRFHeatEIRFT Curve:Biquadratic Object has been added\n", - "VRFHeatEIRFTHi Curve:Biquadratic Object has been added\n", - "CoolingLengthCorrectionFactor Curve:Biquadratic Object has been added\n", - "VRF Piping Correction Factor for Length in Heating Mode Curve:Biquadratic Object has been added\n", - "VRF Heat Recovery Cooling Capacity Modifier Curve:Biquadratic Object has been added\n", - "VRF Heat Recovery Cooling Energy Modifier Curve:Biquadratic Object has been added\n", - "VRF Heat Recovery Heating Capacity Modifier Curve:Biquadratic Object has been added\n", - "VRF Heat Recovery Heating Energy Modifier Curve:Biquadratic Object has been added\n", - "VRFACCoolCapFFF Curve:Quadratic Object has been added\n", - "CoolingEIRHiPLR Curve:Quadratic Object has been added\n", - "VRFCPLFFPLR Curve:Quadratic Object has been added\n", - "HeatingEIRHiPLR Curve:Quadratic Object has been added\n", - "CoolingCombRatio Curve:Linear Object has been added\n", - "HeatingCombRatio Curve:Linear Object has been added\n", - "VRF Outdoor Unit_BLOCK1:ZONE2 AirConditioner:VariableRefrigerantFlow Object has been added\n", - "VRF Outdoor Unit_BLOCK1:ZONE1 AirConditioner:VariableRefrigerantFlow Object has been added\n", - "VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node Object has been added\n", - "VRF Outdoor Unit_BLOCK1:ZONE2 Zone List Object has been added\n", - "VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node Object has been added\n", - "VRF Outdoor Unit_BLOCK1:ZONE1 Zone List Object has been added\n", - "BLOCK1:ZONE2 Sizing:Zone Object has been added\n", - "BLOCK1:ZONE1 Sizing:Zone Object has been added\n", - "BLOCK1:ZONE2 Design Specification Outdoor Air Object has been added\n", - "BLOCK1:ZONE1 Design Specification Outdoor Air Object has been added\n", - "BLOCK1:ZONE2 Design Specification Zone Air Distribution Object has been added\n", - "BLOCK1:ZONE1 Design Specification Zone Air Distribution Object has been added\n", - "BLOCK1:ZONE2 Nodelist Objects has been added\n", - "BLOCK1:ZONE1 Nodelist Objects has been added\n", - "BLOCK1:ZONE2 ZoneHVAC:EquipmentConnections Objects has been added\n", - "BLOCK1:ZONE1 ZoneHVAC:EquipmentConnections Objects has been added\n", - "BLOCK1:ZONE2 ZoneHVAC:EquipmentList Objects has been added\n", - "BLOCK1:ZONE1 ZoneHVAC:EquipmentList Objects has been added\n", - "BLOCK1:ZONE2 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", - "BLOCK1:ZONE1 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", - "BLOCK1:ZONE2 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", - "BLOCK1:ZONE1 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", - "BLOCK1:ZONE2 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", - "BLOCK1:ZONE1 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", - "BLOCK1:ZONE2 Fan:ConstantVolume Object has been added\n", - "BLOCK1:ZONE1 Fan:ConstantVolume Object has been added\n", - "Vent_SP_temp Schedule has been added\n", - "AHST_Sch_BLOCK1_ZONE2 Schedule has been added\n", - "ACST_Sch_BLOCK1_ZONE2 Schedule has been added\n", - "AHST_Sch_BLOCK1_ZONE1 Schedule has been added\n", - "ACST_Sch_BLOCK1_ZONE1 Schedule has been added\n", - "Added - SetComfTemp Program\n", - "Added - CountHours_BLOCK1_ZONE2 Program\n", - "Added - CountHours_BLOCK1_ZONE1 Program\n", - "Added - SetAppLimits Program\n", - "Added - ApplyCAT Program\n", - "Added - SetAST Program\n", - "Added - SetASTnoTol Program\n", - "Added - CountHoursNoApp_BLOCK1_ZONE2 Program\n", - "Added - SetGeoVarBLOCK1_ZONE2 Program\n", - "Added - CountHoursNoApp_BLOCK1_ZONE1 Program\n", - "Added - SetGeoVarBLOCK1_ZONE1 Program\n", - "Added - SetInputData Program\n", - "Added - SetVOFinputData Program\n", - "Added - SetVST Program\n", - "Added - ApplyAST_BLOCK1_ZONE2 Program\n", - "Added - ApplyAST_BLOCK1_ZONE1 Program\n", - "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", - "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", - "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", - "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", - "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", - "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", - "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", - "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", - "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", - "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", - "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", - "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", - "Added - Comfort Temperature Output Variable\n", - "Added - Adaptive Cooling Setpoint Temperature Output Variable\n", - "Added - Adaptive Heating Setpoint Temperature Output Variable\n", - "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Output Variable\n", - "Added - Adaptive Heating Setpoint Temperature_No Tolerance Output Variable\n", - "Added - z_test_ComfStand Output Variable\n", - "Added - z_test_ACSTaul Output Variable\n", - "Added - z_test_ACSTall Output Variable\n", - "Added - z_test_AHSTaul Output Variable\n", - "Added - z_test_AHSTall Output Variable\n", - "Added - z_test_CAT Output Variable\n", - "Added - z_test_ACSToffset Output Variable\n", - "Added - z_test_AHSToffset Output Variable\n", - "Added - z_test_ComfMod Output Variable\n", - "Added - z_test_ACSTtol Output Variable\n", - "Added - z_test_SetpointAcc Output Variable\n", - "Added - z_test_CustAST_m Output Variable\n", - "Added - z_test_CustAST_n Output Variable\n", - "Added - z_test_AHSTtol Output Variable\n", - "Added - Ventilation Setpoint Temperature Output Variable\n", - "Added - Minimum Outdoor Temperature for ventilation Output Variable\n", - "Added - Minimum Outdoor Temperature Difference for ventilation Output Variable\n", - "Added - Maximum Outdoor Temperature Difference for ventilation Output Variable\n", - "Added - Multiplier for Ventilation Opening Factor Output Variable\n", - "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", - "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", - "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Output Variable\n", - "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Output Variable\n", - "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", - "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", - "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", - "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", - "Added - Occupied Hours_BLOCK1_ZONE2 Output Variable\n", - "Added - Occupied Hours_BLOCK1_ZONE1 Output Variable\n", - "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", - "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", - "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", - "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", - "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", - "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", - "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", - "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", - "Added - Zone Floor Area_BLOCK1_ZONE2 Output Variable\n", - "Added - Zone Floor Area_BLOCK1_ZONE1 Output Variable\n", - "Added - Zone Air Volume_BLOCK1_ZONE2 Output Variable\n", - "Added - Zone Air Volume_BLOCK1_ZONE1 Output Variable\n", - "Added - People Occupant Count_BLOCK1_ZONE2 Output Variable\n", - "Added - People Occupant Count_BLOCK1_ZONE1 Output Variable\n", - "Added - Ventilation Hours_BLOCK1_ZONE2 Output Variable\n", - "Added - Ventilation Hours_BLOCK1_ZONE1 Output Variable\n", - "Global variables objects have been added\n", - "Internal variables objects have been added\n", - "Added - RMOT Sensor\n", - "Added - PMOT Sensor\n", - "Added - Occ_count_BLOCK1_ZONE2 Sensor\n", - "Added - Occ_count_BLOCK1_ZONE1 Sensor\n", - "Added - BLOCK1_ZONE2_OpT Sensor\n", - "Added - BLOCK1_ZONE2_WindSpeed Sensor\n", - "Added - BLOCK1_ZONE2_OutT Sensor\n", - "Added - BLOCK1_ZONE1_OpT Sensor\n", - "Added - BLOCK1_ZONE1_WindSpeed Sensor\n", - "Added - BLOCK1_ZONE1_OutT Sensor\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT Sensor\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed Sensor\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT Sensor\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count Sensor\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT Sensor\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed Sensor\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT Sensor\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count Sensor\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", - "Added - OutT Sensor\n", - "Added - AHST_Act_BLOCK1_ZONE2 Actuator\n", - "Added - ACST_Act_BLOCK1:ZONE2 Actuator\n", - "Added - AHST_Act_BLOCK1_ZONE1 Actuator\n", - "Added - ACST_Act_BLOCK1:ZONE1 Actuator\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact Actuator\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact Actuator\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", - "Added - BLOCK1_ZONE2_CoolCoil Sensor\n", - "Added - BLOCK1_ZONE2_HeatCoil Sensor\n", - "Added - BLOCK1_ZONE1_CoolCoil Sensor\n", - "Added - BLOCK1_ZONE1_HeatCoil Sensor\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", - "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil Sensor\n", - "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil Sensor\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", - "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil Sensor\n", - "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil Sensor\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", - "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", - "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", - "Added - SetComfTemp Program Calling Manager\n", - "Added - CountHours_BLOCK1_ZONE2 Program Calling Manager\n", - "Added - CountHours_BLOCK1_ZONE1 Program Calling Manager\n", - "Added - SetAppLimits Program Calling Manager\n", - "Added - ApplyCAT Program Calling Manager\n", - "Added - SetAST Program Calling Manager\n", - "Added - SetASTnoTol Program Calling Manager\n", - "Added - CountHoursNoApp_BLOCK1_ZONE2 Program Calling Manager\n", - "Added - SetGeoVarBLOCK1_ZONE2 Program Calling Manager\n", - "Added - CountHoursNoApp_BLOCK1_ZONE1 Program Calling Manager\n", - "Added - SetGeoVarBLOCK1_ZONE1 Program Calling Manager\n", - "Added - SetInputData Program Calling Manager\n", - "Added - SetVOFinputData Program Calling Manager\n", - "Added - SetVST Program Calling Manager\n", - "Added - ApplyAST_BLOCK1_ZONE2 Program Calling Manager\n", - "Added - ApplyAST_BLOCK1_ZONE1 Program Calling Manager\n", - "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", - "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", - "Added - Comfort Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Adaptive Cooling Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Adaptive Heating Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", - "Added - Adaptive Heating Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_ComfStand Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_ACSTaul Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_ACSTall Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_AHSTaul Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_AHSTall Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_CAT Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_ACSToffset Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_AHSToffset Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_ComfMod Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_ACSTtol Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_SetpointAcc Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_CustAST_m Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_CustAST_n Reporting FrequencyHourly Output:Variable data\n", - "Added - z_test_AHSTtol Reporting FrequencyHourly Output:Variable data\n", - "Added - Ventilation Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Minimum Outdoor Temperature for ventilation Reporting FrequencyHourly Output:Variable data\n", - "Added - Minimum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", - "Added - Maximum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", - "Added - Multiplier for Ventilation Opening Factor Reporting FrequencyHourly Output:Variable data\n", - "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Occupied Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Occupied Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Floor Area_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Floor Area_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Air Volume_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Air Volume_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - People Occupant Count_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - People Occupant Count_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Ventilation Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - Ventilation Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Thermostat Operative Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Thermostat Air Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Operative Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Cooling Coil Total Cooling Rate Reporting FrequencyHourly Output:Variable data\n", - "Added - Heating Coil Heating Rate Reporting FrequencyHourly Output:Variable data\n", - "Added - Facility Total HVAC Electric Demand Power Reporting FrequencyHourly Output:Variable data\n", - "Added - Facility Total HVAC Electricity Demand Rate Reporting FrequencyHourly Output:Variable data\n", - "Added - AFN Surface Venting Window or Door Opening Factor Reporting FrequencyHourly Output:Variable data\n", - "Added - AFN Zone Infiltration Air Change Rate Reporting FrequencyHourly Output:Variable data\n", - "Added - AFN Zone Infiltration Volume Reporting FrequencyHourly Output:Variable data\n", - "Added - AFN Zone Ventilation Air Change Rate Reporting FrequencyHourly Output:Variable data\n", - "Added - AFN Zone Ventilation Volume Reporting FrequencyHourly Output:Variable data\n", - "Added - Site Outdoor Air Drybulb Temperature Reporting FrequencyHourly Output:Variable data\n", - "Added - Site Wind Speed Reporting FrequencyHourly Output:Variable data\n", - "Added - Site Outdoor Air Relative Humidity Reporting FrequencyHourly Output:Variable data\n", - "Added - AHST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - ACST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", - "Added - AHST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - ACST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", - "Added - VRF Heat Pump Cooling Electricity Energy Reporting FrequencyHourly Output:Variable data\n", - "Added - VRF Heat Pump Heating Electricity Energy Reporting FrequencyHourly Output:Variable data\n", - "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", - "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", - "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", - "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", - "Added - OutputControl:Files object\n", - "Added - Output:VariableDictionary object\n", - "\n", - "=======================END OF OUTPUT IDF FILE GENERATION PROCESS=======================\n", - "\n" - ] - } - ], - "source": [ - "parametric = OptimParamSimulation(\n", - " building=building,\n", - " parameters_type='accim predefined model',\n", - " #output_type='standard', #\n", - " #output_keep_existing=False, #\n", - " #output_freqs=['hourly'], #\n", - " #ScriptType='vrf_mm', #\n", - " #SupplyAirTempInputMethod='temperature difference', #\n", - " #debugging=True, #\n", - " #verbosemode=False #\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "51eed0eb-7b3a-4428-8952-070a650acf61", - "metadata": {}, - "source": [ - "An initial and generic version of the Adaptive-Comfort-Control-Implementation Script (ACCIS) has been added to the idf instance `building`. For instance, you can take a look at the parameter values accis currently has:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ad387b77-4998-4e51-979a-928fa52e551d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[\n", - " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", - " SetInputData, !- Name\n", - " set ComfStand = 1, !- Program Line 1\n", - " set CAT = 1, !- Program Line 2\n", - " set ComfMod = 2, !- Program Line 3\n", - " set HVACmode = 2, !- Program Line 4\n", - " set VentCtrl = 0, !- Program Line 5\n", - " set VSToffset = 0, !- Program Line 6\n", - " set MinOToffset = 7, !- Program Line 7\n", - " set MaxWindSpeed = 6, !- Program Line 8\n", - " set ACSTtol = -0.25, !- Program Line 9\n", - " set AHSTtol = 0.25, !- Program Line 10\n", - " set CoolSeasonStart = 121, !- Program Line 11\n", - " set CoolSeasonEnd = 274; !- Program Line 12]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata']" - ] - }, - { - "cell_type": "markdown", - "id": "99605e82-4d01-4aee-835c-ab9c6c66faa4", - "metadata": {}, - "source": [ - "## Setting the outputs" - ] - }, - { - "cell_type": "markdown", - "id": "bc714539-79c3-46e9-9caa-44a21fc413cb", - "metadata": {}, - "source": [ - "### Outputs for the idf (i.e. the outputs for each simulation run)" - ] - }, - { - "cell_type": "markdown", - "id": "764b7626-0e63-4317-8bbf-28ac2fe68f68", - "metadata": {}, - "source": [ - "First of all, we are going to set the outputs of the simulations that are going to be performed. This is an important step, especially if you are going to run hundreds or thousands of simulations." - ] - }, - { - "cell_type": "markdown", - "id": "b2f1d34a-dc0a-4aac-b01f-a7425b95090f", - "metadata": {}, - "source": [ - "Let's take a look at the Output:Variable objects we currently have in the idf. The method `get_output_var_df_from_idf()` returns a pandas DataFrame which contains the information of the existing Output:Variable objects in the idf:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "12ee78ba-c60b-4ef1-a6fb-51086a8a53fa", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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key_valuevariable_namereporting_frequencyschedule_name
0*Comfort TemperatureHourly
1*Adaptive Cooling Setpoint TemperatureHourly
2*Adaptive Heating Setpoint TemperatureHourly
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
...............
72*VRF Heat Pump Heating Electricity EnergyHourly
73BLOCK1_ZONE2 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
74BLOCK1_ZONE2 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
75BLOCK1_ZONE1 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
76BLOCK1_ZONE1 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
\n", - "

77 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " key_value \\\n", - "0 * \n", - "1 * \n", - "2 * \n", - "3 * \n", - "4 * \n", - ".. ... \n", - "72 * \n", - "73 BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil \n", - "74 BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil \n", - "75 BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil \n", - "76 BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil \n", - "\n", - " variable_name reporting_frequency \\\n", - "0 Comfort Temperature Hourly \n", - "1 Adaptive Cooling Setpoint Temperature Hourly \n", - "2 Adaptive Heating Setpoint Temperature Hourly \n", - "3 Adaptive Cooling Setpoint Temperature_No Toler... Hourly \n", - "4 Adaptive Heating Setpoint Temperature_No Toler... Hourly \n", - ".. ... ... \n", - "72 VRF Heat Pump Heating Electricity Energy Hourly \n", - "73 Cooling Coil Total Cooling Rate Hourly \n", - "74 Heating Coil Heating Rate Hourly \n", - "75 Cooling Coil Total Cooling Rate Hourly \n", - "76 Heating Coil Heating Rate Hourly \n", - "\n", - " schedule_name \n", - "0 \n", - "1 \n", - "2 \n", - "3 \n", - "4 \n", - ".. ... \n", - "72 \n", - "73 \n", - "74 \n", - "75 \n", - "76 \n", - "\n", - "[77 rows x 4 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_output_variables_idf = parametric.get_output_var_df_from_idf()\n", - "df_output_variables_idf" - ] - }, - { - "cell_type": "markdown", - "id": "ab1e81e8-c2f0-40c0-b868-0cf7ddb9954f", - "metadata": {}, - "source": [ - "now, let's see the Output:Meter objects:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "94c9844b-aacf-41fa-b1d4-ac7b491aa14e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
key_namefrequency
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [key_name, frequency]\n", - "Index: []" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", - "df_output_meters_idf.head()" - ] - }, - { - "cell_type": "markdown", - "id": "093bdcab-a1aa-492a-8864-d2e4e1a0b72c", - "metadata": {}, - "source": [ - "In this case, we can see there is no Output:Meter. However, there is a large number of Output:Variable objects which might result in heavy simulation outputs. So, let's get rid of some of them. We can drop the rows we want, and then input the modified DataFrame in the method `set_output_var_df_to_idf(outputs_df)`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9ccc7b08-119c-4f4c-bb45-9aac8201fe7e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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key_valuevariable_namereporting_frequencyschedule_name
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
52*Zone Operative TemperatureHourly
54*Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
\n", - "
" - ], - "text/plain": [ - " key_value variable_name \\\n", - "3 * Adaptive Cooling Setpoint Temperature_No Toler... \n", - "4 * Adaptive Heating Setpoint Temperature_No Toler... \n", - "52 * Zone Operative Temperature \n", - "54 * Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", - "\n", - " reporting_frequency schedule_name \n", - "3 Hourly \n", - "4 Hourly \n", - "52 Hourly \n", - "54 Hourly " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_output_variables_idf = df_output_variables_idf[\n", - " (\n", - " df_output_variables_idf['variable_name'].str.contains('Setpoint Temperature_No Tolerance')\n", - " |\n", - " df_output_variables_idf['variable_name'].str.contains('Zone Operative Temperature')\n", - " |\n", - " df_output_variables_idf['variable_name'].str.contains('Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')\n", - " )\n", - "]\n", - "df_output_variables_idf" - ] - }, - { - "cell_type": "markdown", - "id": "643151b4-a20d-4d00-a1c9-5a78a56be916", - "metadata": {}, - "source": [ - "Let's keep only the Output:Variable objects we have filtered using the `set_output_var_df_to_idf(outputs_df)`:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8958bc66-f42d-4fc0-9622-f2b3ffb03852", - "metadata": {}, - "outputs": [], - "source": [ - "parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf)" - ] - }, - { - "cell_type": "markdown", - "id": "eacd9d6d-7fa1-4e6d-8bab-edbfa322140b", - "metadata": {}, - "source": [ - "We have removed all rows except the adaptive heating and cooling setpoints, the operative temperature and the running mean outdoor temperature. Next optional step is adding Output:Meter objects. We can do that using the method `set_output_met_objects_to_idf(output_meters)`, where `output_meters` is a list of Output:Meter key names." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "addf36d3-b7c2-4254-bec5-322850a464d1", - "metadata": {}, - "outputs": [], - "source": [ - "output_meters = [\n", - " 'Heating:Electricity',\n", - " 'Cooling:Electricity',\n", - " 'Electricity:HVAC',\n", - "]\n", - "parametric.set_output_met_objects_to_idf(output_meters=output_meters)" - ] - }, - { - "cell_type": "markdown", - "id": "fa66a111-e72e-4ebd-8110-8222191d6c5d", - "metadata": {}, - "source": [ - "Let's see Output:Meter objects we currently have after adding these:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "41878d11-6dcc-4158-9ec4-a74aa9d2dafc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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key_namefrequency
0Heating:Electricityhourly
1Cooling:Electricityhourly
2Electricity:HVAChourly
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" - ], - "text/plain": [ - " key_name frequency\n", - "0 Heating:Electricity hourly\n", - "1 Cooling:Electricity hourly\n", - "2 Electricity:HVAC hourly" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", - "df_output_meters_idf.head()" - ] - }, - { - "cell_type": "markdown", - "id": "90fd8d9b-ecd8-4c79-9f37-775f2c2921ed", - "metadata": {}, - "source": [ - "### Outputs to be read and shown in the parametric simulation or optimisation" - ] - }, - { - "cell_type": "markdown", - "id": "bb18fd43-e07e-42f1-ac75-020bcbe96d9a", - "metadata": {}, - "source": [ - "To successfully run the parametric simulation or optimisation, it is advisable running a test simulation to know the outputs that each simulation will have. We can do that with the method `get_outputs_df_from_testsim()`, which returns a tuple containing 2 DataFrames containing respectively the Output:Meter and Output:Variable objects from the simulation. In this case, you won't find wildcards such as \"*\"." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ff6093e9-840d-4142-ad79-98d78f4f13d1", - "metadata": {}, - "outputs": [], - "source": [ - "df_output_meters_testsim, df_output_variables_testsim = parametric.get_outputs_df_from_testsim()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "61a88134-7997-42ce-88b0-ef4606cdfe61", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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key_namefrequency
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1Cooling:ElectricityHourly
2Electricity:HVACHourly
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key_valuevariable_namefrequency
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly
2BLOCK1:ZONE2Zone Operative TemperatureHourly
3BLOCK1:ZONE1Zone Operative TemperatureHourly
4PEOPLE BLOCK1:ZONE2Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" - ], - "text/plain": [ - " key_value variable_name \\\n", - "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", - "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", - "2 BLOCK1:ZONE2 Zone Operative Temperature \n", - "3 BLOCK1:ZONE1 Zone Operative Temperature \n", - "4 PEOPLE BLOCK1:ZONE2 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", - "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", - "\n", - " frequency \n", - "0 Hourly \n", - "1 Hourly \n", - "2 Hourly \n", - "3 Hourly \n", - "4 Hourly \n", - "5 Hourly " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_output_variables_testsim" - ] - }, - { - "cell_type": "markdown", - "id": "b0ed4500-6054-4a00-ae63-54412bf70944", - "metadata": {}, - "source": [ - "We can get DataFrames from the .rdd and .mdd files generated from the test simulation using the functions `get_rdd_file_as_df()` and `get_mdd_file_as_df()`. " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e28454a5-8de7-4551-bf9b-4db3fa8f124f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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objectkey_valuevariable_namefrequencyunits
0Output:Variable*Site Outdoor Air Drybulb Temperaturehourly!- Zone Average [C]
1Output:Variable*Site Outdoor Air Dewpoint Temperaturehourly!- Zone Average [C]
2Output:Variable*Site Outdoor Air Wetbulb Temperaturehourly!- Zone Average [C]
3Output:Variable*Site Outdoor Air Humidity Ratiohourly!- Zone Average [kgWater/kgDryAir]
4Output:Variable*Site Outdoor Air Relative Humidityhourly!- Zone Average [%]
..................
712Output:Variable*Zone Ventilation When Unoccupied Timehourly!- HVAC Sum [hr]
713Output:Variable*Facility Any Zone Ventilation Below Target Voz...hourly!- HVAC Sum [hr]
714Output:Variable*Facility All Zones Ventilation At Target Voz Timehourly!- HVAC Sum [hr]
715Output:Variable*Facility Any Zone Ventilation Above Target Voz...hourly!- HVAC Sum [hr]
716Output:Variable*Facility Any Zone Ventilation When Unoccupied ...hourly!- HVAC Sum [hr]
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717 rows × 5 columns

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" - ], - "text/plain": [ - " object key_value \\\n", - "0 Output:Variable * \n", - "1 Output:Variable * \n", - "2 Output:Variable * \n", - "3 Output:Variable * \n", - "4 Output:Variable * \n", - ".. ... ... \n", - "712 Output:Variable * \n", - "713 Output:Variable * \n", - "714 Output:Variable * \n", - "715 Output:Variable * \n", - "716 Output:Variable * \n", - "\n", - " variable_name frequency \\\n", - "0 Site Outdoor Air Drybulb Temperature hourly \n", - "1 Site Outdoor Air Dewpoint Temperature hourly \n", - "2 Site Outdoor Air Wetbulb Temperature hourly \n", - "3 Site Outdoor Air Humidity Ratio hourly \n", - "4 Site Outdoor Air Relative Humidity hourly \n", - ".. ... ... \n", - "712 Zone Ventilation When Unoccupied Time hourly \n", - "713 Facility Any Zone Ventilation Below Target Voz... hourly \n", - "714 Facility All Zones Ventilation At Target Voz Time hourly \n", - "715 Facility Any Zone Ventilation Above Target Voz... hourly \n", - "716 Facility Any Zone Ventilation When Unoccupied ... hourly \n", - "\n", - " units \n", - "0 !- Zone Average [C] \n", - "1 !- Zone Average [C] \n", - "2 !- Zone Average [C] \n", - "3 !- Zone Average [kgWater/kgDryAir] \n", - "4 !- Zone Average [%] \n", - ".. ... \n", - "712 !- HVAC Sum [hr] \n", - "713 !- HVAC Sum [hr] \n", - "714 !- HVAC Sum [hr] \n", - "715 !- HVAC Sum [hr] \n", - "716 !- HVAC Sum [hr] \n", - "\n", - "[717 rows x 5 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_rdd = get_rdd_file_as_df()\n", - "df_rdd" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "58649f6e-f08c-49e5-823a-376ff26eb83a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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objectmeter_namefrequencyunits
0Output:MeterElectricity:Facilityhourly!- [J]
1Output:Meter:CumulativeElectricity:Facilityhourly!- [J]
2Output:MeterElectricity:Buildinghourly!- [J]
3Output:Meter:CumulativeElectricity:Buildinghourly!- [J]
4Output:MeterElectricity:Zone:BLOCK1:ZONE2hourly!- [J]
...............
157Output:Meter:CumulativeGeneral:HeatRecovery:EnergyTransferhourly!- [J]
158Output:MeterCarbon Equivalent:Facilityhourly!- [kg]
159Output:Meter:CumulativeCarbon Equivalent:Facilityhourly!- [kg]
160Output:MeterCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
161Output:Meter:CumulativeCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
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162 rows × 4 columns

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" - ], - "text/plain": [ - " object meter_name \\\n", - "0 Output:Meter Electricity:Facility \n", - "1 Output:Meter:Cumulative Electricity:Facility \n", - "2 Output:Meter Electricity:Building \n", - "3 Output:Meter:Cumulative Electricity:Building \n", - "4 Output:Meter Electricity:Zone:BLOCK1:ZONE2 \n", - ".. ... ... \n", - "157 Output:Meter:Cumulative General:HeatRecovery:EnergyTransfer \n", - "158 Output:Meter Carbon Equivalent:Facility \n", - "159 Output:Meter:Cumulative Carbon Equivalent:Facility \n", - "160 Output:Meter CarbonEquivalentEmissions:Carbon Equivalent \n", - "161 Output:Meter:Cumulative CarbonEquivalentEmissions:Carbon Equivalent \n", - "\n", - " frequency units \n", - "0 hourly !- [J] \n", - "1 hourly !- [J] \n", - "2 hourly !- [J] \n", - "3 hourly !- [J] \n", - "4 hourly !- [J] \n", - ".. ... ... \n", - "157 hourly !- [J] \n", - "158 hourly !- [kg] \n", - "159 hourly !- [kg] \n", - "160 hourly !- [kg] \n", - "161 hourly !- [kg] \n", - "\n", - "[162 rows x 4 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_mdd = get_mdd_file_as_df()\n", - "df_mdd" - ] - }, - { - "cell_type": "markdown", - "id": "a84943b5-db09-4f19-95bb-f9583711d0a7", - "metadata": {}, - "source": [ - "Also, we can parse the .mtd files as a list using the function `parse_mtd_file()`." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "df4da1a7-fcb2-4c59-b790-1bc9e3b128f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'meter_id': '14',\n", - " 'description': 'BLOCK1:ZONE2 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", - " 'on_meters': ['Electricity:Facility [J]',\n", - " 'Electricity:Building [J]',\n", - " 'Electricity:Zone:BLOCK1:ZONE2 [J]',\n", - " 'Electricity:SpaceType:GENERAL [J]',\n", - " 'InteriorLights:Electricity [J]',\n", - " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", - " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", - " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity [J]',\n", - " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", - " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']},\n", - " {'meter_id': '135',\n", - " 'description': 'BLOCK1:ZONE1 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", - " 'on_meters': ['Electricity:Facility [J]',\n", - " 'Electricity:Building [J]',\n", - " 'Electricity:Zone:BLOCK1:ZONE1 [J]',\n", - " 'Electricity:SpaceType:GENERAL [J]',\n", - " 'InteriorLights:Electricity [J]',\n", - " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", - " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", - " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity [J]',\n", - " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", - " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']}]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mtd_list = parse_mtd_file()\n", - "mtd_list[0:2]" - ] - }, - { - "cell_type": "markdown", - "id": "76fa398d-15a0-4504-9f11-468bfd7c094f", - "metadata": {}, - "source": [ - "Therefore, we have 2 DataFrames, one for the Output:Meter and another for the Output:Variable objects. Next step is setting the outputs for the parametric simulation. To do so, we'll need to pass the DataFrames into the method `set_outputs_for_simulation(df_output_meter, df_output_variable)`. If you have some knowledge about the python package besos, you might think of these dataframes as if each row was a `MeterReader` or `VariableReader` instances respectively for the Output:Meter and Output:Variable dataframes, and the arguments in these were the specified in the columns. The `MeterReader` class takes the arguments `key_name`, `frequency`, `name` and `func`, while `VariableReader` class takes the arguments `key_value`, `variable_name`, `frequency`, `name` and `func`." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2b133883-a0be-42a9-bc04-0e6054337b9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['key_name', 'frequency']" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[i for i in df_output_meters_testsim.columns]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c43b0496-1d36-4a0e-b2dc-9f77e41627ff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['key_value', 'variable_name', 'frequency']" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[i for i in df_output_variables_testsim.columns]" - ] - }, - { - "cell_type": "markdown", - "id": "a03bd526-8bdf-4cce-af62-73ac36e9d0c3", - "metadata": {}, - "source": [ - "If you take a look at the columns of the dataframes above, you can see the names are the arguments in the `MeterReader` and `VariableReader` classes, and only `name` and `func` are missing. That means, you can add these columns to input the `name` and `func` arguments as desired. In case of the Output:Meter dataframe, we won't add the `name` and `func` columns, which means the name will be the `key_name` and hourly results will be aggregated using the pd.Series.sum() function. However, in case of the Output:Variable dataframe, we will specify these: we want the hourly values rather than the aggregation, therefore we will pass the name bound to the function `return_time_series`, and we will add '_time series' as a suffix to the `variable_name` column. We will also remove the outputs for BLOCK1:ZONE2, which are the rows 2 and 4." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0e8fecdc-4625-4d95-a87d-67071884963b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x000002427114...Adaptive Cooling Setpoint Temperature_No Toler...
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x000002427114...Adaptive Heating Setpoint Temperature_No Toler...
3BLOCK1:ZONE1Zone Operative TemperatureHourly<function return_time_series at 0x000002427114...Zone Operative Temperature_time series
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly<function return_time_series at 0x000002427114...Zone Thermal Comfort ASHRAE 55 Adaptive Model ...
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" - ], - "text/plain": [ - " key_value variable_name \\\n", - "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", - "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", - "3 BLOCK1:ZONE1 Zone Operative Temperature \n", - "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", - "\n", - " frequency func \\\n", - "0 Hourly , key_name='Heating:Electricity'),\n", - " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", - " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", - " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", - " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", - " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", - " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parametric.sim_outputs" - ] - }, - { - "cell_type": "markdown", - "id": "b5dd98bb-6b1d-4b3c-b762-3c9a0c015f63", - "metadata": {}, - "source": [ - "## Setting the parameters" - ] - }, - { - "cell_type": "markdown", - "id": "4804beed-52e5-432a-a999-685c222d668d", - "metadata": {}, - "source": [ - "At the top of the script, when you instantiated the class `OptimParamSimulation`, you already specified which type of parameters you were going to use. Now, the parameters we're about to set, must match the `parameters_type` argument. At this point, you may not know which parameters you can use, so you can call the method `get_available_parameters()`, which will return a list of available parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "004029fa-ff72-4f43-a9c3-e004757463e4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['ComfStand',\n", - " 'CAT',\n", - " 'CATcoolOffset',\n", - " 'CATheatOffset',\n", - " 'ComfMod',\n", - " 'SetpointAcc',\n", - " 'CoolSeasonStart',\n", - " 'CoolSeasonEnd',\n", - " 'HVACmode',\n", - " 'VentCtrl',\n", - " 'MaxTempDiffVOF',\n", - " 'MinTempDiffVOF',\n", - " 'MultiplierVOF',\n", - " 'VSToffset',\n", - " 'MinOToffset',\n", - " 'MaxWindSpeed',\n", - " 'ASTtol']" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "available_parameters = parametric.get_available_parameters()\n", - "available_parameters" - ] - }, - { - "cell_type": "markdown", - "id": "45886126-30b8-4e53-b8c0-06135e6a3c38", - "metadata": {}, - "source": [ - "If you don't know what are these, please refer to the [documentation](https://accim.readthedocs.io/en/master/4_detailed%20use.html).\n", - "\n", - "Using the 'accim predefined model' type, the values must be a list of options, since the values for some arguments are used to select some specific comfort model, and have a categorical use rather than numeric. Now, let's set the parameters using the method `set_parameters(accis_params_dict, additional_params)`. In this method, we set the parameters related to accim using the argument `accis_params_dict`, which takes a dictionary following the pattern {'parameter name': [1, 2, 3, etc]}. We can also add some other parameters not related to accim in the argument `additional_params`, which takes a list of parameters as if these were input straight to the besos EPProblem class. If additional arguments are added, the descriptors must be `CategoryParameter`." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "b4eb43dd-879b-4175-ab56-e9c04ea342af", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named ComfStand.The name used as an input (ComfStand) will be discarded.\n", - " warnings.warn(\n", - "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CAT.The name used as an input (CAT) will be discarded.\n", - " warnings.warn(\n", - "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named ComfMod.The name used as an input (ComfMod) will be discarded.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "accis_parameters = {\n", - " 'ComfStand': [1, 2],\n", - " 'CAT': [80, 90],\n", - " 'ComfMod': [3],\n", - "}\n", - "parametric.set_parameters(accis_params_dict=accis_parameters)" - ] - }, - { - "cell_type": "markdown", - "id": "b2e1e8a5-4af9-4d23-82b9-af45b49ba26d", - "metadata": {}, - "source": [ - "If you want to inspect the `Parameter` objects, you can see the internal variable `parameters_list`:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "4b72a5b5-417f-410b-b30a-bf6550dbb364", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]),\n", - " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]),\n", - " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parametric.parameters_list" - ] - }, - { - "cell_type": "markdown", - "id": "16b3b170-683d-49b3-9d6b-4d3772245647", - "metadata": {}, - "source": [ - "## Running the parametric simulation" - ] - }, - { - "cell_type": "markdown", - "id": "e36956e3-781b-4fe1-980f-ad9fbca21fba", - "metadata": {}, - "source": [ - "### Setting the problem" - ] - }, - { - "cell_type": "markdown", - "id": "60150ec0-8c63-4097-bd4f-c8263dc22d47", - "metadata": {}, - "source": [ - "First, let's set the problem. To do so, use the `set_problem()` method. In case of the parametric simulation you don't need to input any argument. However, in case of the optimisation, you must input the arguments `minimize_outputs`, `constraints` and `constraint_bounds`, similarly as you would do in the besos `EPProblem` class." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "cd2c3ebc-99ea-46a2-94cf-37b3ddd77630", - "metadata": {}, - "outputs": [], - "source": [ - "parametric.set_problem()" - ] - }, - { - "cell_type": "markdown", - "id": "cc1eac32-9784-441f-98e3-0382053ba14d", - "metadata": {}, - "source": [ - "Again, you can inspect the `EPProblem` class instance in the internal variable `problem`:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "a4f80f41-febc-47e0-b610-8c07893a5c20", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parametric.problem" - ] - }, - { - "cell_type": "markdown", - "id": "901b319a-2ef8-4c7c-9fea-7512b81807f0", - "metadata": {}, - "source": [ - "### Sampling the simulation runs" - ] - }, - { - "cell_type": "markdown", - "id": "cecbc846-7227-498a-b0f8-f8795269df25", - "metadata": {}, - "source": [ - "The way to inform besos of the variations and permutations it must carry out in the parametric analysis is by means of a DataFrame, which must contain a column per `Parameter`, in which values are specified. There are multiple ways to do this DataFrame. For instance, we could make a dataframe from scratch:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "30f78385-766d-47f2-b394-c922f8403b30", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " ComfStand CAT ComfMod\n", - "0 2 80 3\n", - "1 2 90 3" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "param_dict = {'ComfStand': [2, 2], 'CAT': [80, 90], 'ComfMod': [3, 3]}\n", - "input_param_df = pd.DataFrame(data=param_dict)\n", - "input_param_df" - ] - }, - { - "cell_type": "markdown", - "id": "9a525ce0-8b59-48ca-a160-59ff4781a8bb", - "metadata": {}, - "source": [ - "Also, we can use the sampling functions from besos (`full_factorial` and `lhs`), although these are not available using the accim predefined models, since these are based on ranges of values instead of options. In this case, we could use the `sampling_full_set()` method, which will combine all the values we entered when we set the parameters and drop the invalid combinations (e.g. you cannot use CAT=1, 2 or 3 if you are using ComfStand=2, since these CAT values are only available for ComfStand=1)." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "286da8cc-4ee2-41b3-9d3a-07ba98f708e0", - "metadata": {}, - "outputs": [], - "source": [ - "parametric.sampling_full_set()" - ] - }, - { - "cell_type": "markdown", - "id": "4f55e72f-f2b7-4f8a-940e-af5d23e0aa72", - "metadata": {}, - "source": [ - "Now, you can see the resulting input parameter dataframe in the internal variable `parameters_values_df`:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "dfebdd41-ee54-4fcf-ab7e-12db7e9461fb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ComfStandCATComfMod
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Therefore, we are going to set the occupancy to always on by means of the function `accim.utils.set_occupancy_to_always`, in which we input the IDF class instance we read in the previous cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f5c2feb1-ca1e-4b8a-bc50-1f116cca9fa4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On 24/7 Schedule:Compact object was already in the model.\n", + "People Block1:Zone2 Number of People Schedule Name has been set to always occupied.\n", + "People Block1:Zone1 Number of People Schedule Name has been set to always occupied.\n" + ] + } + ], + "source": [ + "accim.utils.set_occupancy_to_always(idf_object=building)" + ] + }, + { + "cell_type": "markdown", + "id": "29827b0f-9086-4e26-873b-db2f7818a18a", + "metadata": {}, + "source": [ + "Now, let's start with the settings for the parametric analysis. First, let's instantiate the class `OptimParamSimulation`, and let's pass the IDF instance in the argument `building`. Argument `parameters_type` can take 3 different strings:\n", + "- \"accim predefined model\", in which models are those previously defined in accim (ComfStand=0 to ComfStand=22);\n", + "- \"accim custom model\", in which key parameters of the adaptive comfort model are defined in the relevant arguments;\n", + "- \"apmv setpoints\", in which setpoints are based on the aPMV (Adaptive Predicted Mean Vote) instead of the PMV index;\n", + "\n", + "In this case, we're going to use the 'accim custom model' type, in which we can define the adaptive comfort model." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2a5fa261-bfee-4b08-a8fe-c62f46a481eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------\n", + "Adaptive-Comfort-Control-Implemented Model (ACCIM) v0.7.5\n", + "--------------------------------------------------------\n", + "\n", + "This tool allows to apply adaptive setpoint temperatures. \n", + "For further information, please read the documentation: \n", + "https://accim.readthedocs.io/en/master/\n", + "For a visual understanding of the tool, please visit the following jupyter notebooks:\n", + "- Using addAccis() to apply adaptive setpoint temperatures\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/addAccis/using_addAccis.html\n", + "- Using rename_epw_files() to rename the EPWs for proper data analysis after simulation\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/rename_epw_files/using_rename_epw_files.html\n", + "- Using runEp() to directly run simulations with EnergyPlus\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/runEp/using_runEp.html\n", + "- Using the class Table() for data analysis\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/Table/using_Table.html\n", + "- Full example\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/full_example/full_example.html\n", + "\n", + "Starting with the process.\n", + "Basic input data:\n", + "ScriptType is: vrf_mm\n", + "Supply Air Temperature Input Method is: temperature difference\n", + "Output type is: standard\n", + "Output frequencies are: \n", + "['hourly']\n", + "EnergyPlus version is: 23.1\n", + "Temperature Control method is: temperature\n", + "\n", + "=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================\n", + "\n", + "Starting with file:\n", + "The occupied zones in the model are:\n", + "BLOCK1:ZONE2\n", + "BLOCK1:ZONE1\n", + "The windows and doors in the model are:\n", + "Block1_Zone2_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_4_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_5_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_2_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_5_0_0_0_0_0_Win\n", + "The zones in the model are:\n", + "BLOCK1_ZONE2\n", + "BLOCK1_ZONE1\n", + "The people objects in the model have been amended.\n", + "BLOCK1:ZONE2 Thermostat has been added\n", + "BLOCK1:ZONE1 Thermostat has been added\n", + "On Schedule already was in the model\n", + "TypOperativeTempControlSch Schedule already was in the model\n", + "All ZoneHVAC:IdealLoadsAirSystem Heating and Cooling availability schedules has been set to on\n", + "On 24/7 Schedule already was in the model\n", + "Control type schedule: Always 4 Schedule has been added\n", + "Relative humidity setpoint schedule: Always 50.00 Schedule has been added\n", + "Heating Fanger comfort setpoint: Always -0.5 Schedule has been added\n", + "Cooling Fanger comfort setpoint: Always 0.1 Schedule has been added\n", + "Zone CO2 setpoint: Always 900ppm Schedule has been added\n", + "Min CO2 concentration: Always 600ppm Schedule has been added\n", + "Generic contaminant setpoint: Always 0.5ppm Schedule has been added\n", + "Air distribution effectiveness (always 1) Schedule has been added\n", + "VRF Heating Cooling (Northern Hemisphere) Schedule has been added\n", + "DefaultFanEffRatioCurve Curve:Cubic Object has been added\n", + "VRFTUCoolCapFT Curve:Cubic Object has been added\n", + "VRFTUHeatCapFT Curve:Cubic Object has been added\n", + "VRFCoolCapFTBoundary Curve:Cubic Object has been added\n", + "VRFCoolEIRFTBoundary Curve:Cubic Object has been added\n", + "CoolingEIRLowPLR Curve:Cubic Object has been added\n", + "VRFHeatCapFTBoundary Curve:Cubic Object has been added\n", + "VRFHeatEIRFTBoundary Curve:Cubic Object has been added\n", + "HeatingEIRLowPLR Curve:Cubic Object has been added\n", + "DefaultFanPowerRatioCurve Curve:Exponent Object has been added\n", + "DXHtgCoilDefrostEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFTHi Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatCapFT Curve:Biquadratic Object has been added\n", + "VRFHeatCapFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFT Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFTHi Curve:Biquadratic Object has been added\n", + "CoolingLengthCorrectionFactor Curve:Biquadratic Object has been added\n", + "VRF Piping Correction Factor for Length in Heating Mode Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Energy Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Energy Modifier Curve:Biquadratic Object has been added\n", + "VRFACCoolCapFFF Curve:Quadratic Object has been added\n", + "CoolingEIRHiPLR Curve:Quadratic Object has been added\n", + "VRFCPLFFPLR Curve:Quadratic Object has been added\n", + "HeatingEIRHiPLR Curve:Quadratic Object has been added\n", + "CoolingCombRatio Curve:Linear Object has been added\n", + "HeatingCombRatio Curve:Linear Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Zone List Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Zone List Object has been added\n", + "BLOCK1:ZONE2 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE1 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE2 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE1 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE2 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE1 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE2 Nodelist Objects has been added\n", + "BLOCK1:ZONE1 Nodelist Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Fan:ConstantVolume Object has been added\n", + "BLOCK1:ZONE1 Fan:ConstantVolume Object has been added\n", + "Vent_SP_temp Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "Added - SetComfTemp Program\n", + "Added - CountHours_BLOCK1_ZONE2 Program\n", + "Added - CountHours_BLOCK1_ZONE1 Program\n", + "Added - SetAppLimits Program\n", + "Added - ApplyCAT Program\n", + "Added - SetAST Program\n", + "Added - SetASTnoTol Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program\n", + "Added - SetInputData Program\n", + "Added - SetVOFinputData Program\n", + "Added - SetVST Program\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - Comfort Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Output Variable\n", + "Added - z_test_ComfStand Output Variable\n", + "Added - z_test_ACSTaul Output Variable\n", + "Added - z_test_ACSTall Output Variable\n", + "Added - z_test_AHSTaul Output Variable\n", + "Added - z_test_AHSTall Output Variable\n", + "Added - z_test_CAT Output Variable\n", + "Added - z_test_ACSToffset Output Variable\n", + "Added - z_test_AHSToffset Output Variable\n", + "Added - z_test_ComfMod Output Variable\n", + "Added - z_test_ACSTtol Output Variable\n", + "Added - z_test_SetpointAcc Output Variable\n", + "Added - z_test_CustAST_m Output Variable\n", + "Added - z_test_CustAST_n Output Variable\n", + "Added - z_test_AHSTtol Output Variable\n", + "Added - Ventilation Setpoint Temperature Output Variable\n", + "Added - Minimum Outdoor Temperature for ventilation Output Variable\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Multiplier for Ventilation Opening Factor Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Output Variable\n", + "Global variables objects have been added\n", + "Internal variables objects have been added\n", + "Added - RMOT Sensor\n", + "Added - PMOT Sensor\n", + "Added - Occ_count_BLOCK1_ZONE2 Sensor\n", + "Added - Occ_count_BLOCK1_ZONE1 Sensor\n", + "Added - BLOCK1_ZONE2_OpT Sensor\n", + "Added - BLOCK1_ZONE2_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE2_OutT Sensor\n", + "Added - BLOCK1_ZONE1_OpT Sensor\n", + "Added - BLOCK1_ZONE1_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE1_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - OutT Sensor\n", + "Added - AHST_Act_BLOCK1_ZONE2 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE2 Actuator\n", + "Added - AHST_Act_BLOCK1_ZONE1 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE1 Actuator\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - BLOCK1_ZONE2_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE2_HeatCoil Sensor\n", + "Added - BLOCK1_ZONE1_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE1_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - SetComfTemp Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetAppLimits Program Calling Manager\n", + "Added - ApplyCAT Program Calling Manager\n", + "Added - SetAST Program Calling Manager\n", + "Added - SetASTnoTol Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetInputData Program Calling Manager\n", + "Added - SetVOFinputData Program Calling Manager\n", + "Added - SetVST Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - Comfort Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfStand Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CAT Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfMod Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_SetpointAcc Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_m Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_n Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Multiplier for Ventilation Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Cooling Coil Total Cooling Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Heating Coil Heating Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electric Demand Power Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electricity Demand Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Surface Venting Window or Door Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Drybulb Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Wind Speed Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Relative Humidity Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Cooling Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Heating Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - OutputControl:Files object\n", + "Added - Output:VariableDictionary object\n", + "\n", + "=======================END OF OUTPUT IDF FILE GENERATION PROCESS=======================\n", + "\n" + ] + } + ], + "source": [ + "parametric = OptimParamSimulation(\n", + " building=building,\n", + " parameters_type='accim custom model',\n", + " #output_type='standard', #\n", + " #output_keep_existing=False, #\n", + " #output_freqs=['hourly'], #\n", + " #ScriptType='vrf_mm', #\n", + " #SupplyAirTempInputMethod='temperature difference', #\n", + " #debugging=True, #\n", + " #verbosemode=False #\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51eed0eb-7b3a-4428-8952-070a650acf61", + "metadata": {}, + "source": [ + "An initial and generic version of the Adaptive-Comfort-Control-Implementation Script (ACCIS) has been added to the idf instance `building`. For instance, you can take a look at the parameter values accis currently has:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad387b77-4998-4e51-979a-928fa52e551d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetInputData, !- Name\n", + " set ComfStand = 1, !- Program Line 1\n", + " set CAT = 1, !- Program Line 2\n", + " set ComfMod = 2, !- Program Line 3\n", + " set HVACmode = 2, !- Program Line 4\n", + " set VentCtrl = 0, !- Program Line 5\n", + " set VSToffset = 0, !- Program Line 6\n", + " set MinOToffset = 7, !- Program Line 7\n", + " set MaxWindSpeed = 6, !- Program Line 8\n", + " set ACSTtol = -0.25, !- Program Line 9\n", + " set AHSTtol = 0.25, !- Program Line 10\n", + " set CoolSeasonStart = 121, !- Program Line 11\n", + " set CoolSeasonEnd = 274; !- Program Line 12]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata']" + ] + }, + { + "cell_type": "markdown", + "id": "99605e82-4d01-4aee-835c-ab9c6c66faa4", + "metadata": {}, + "source": [ + "## Setting the outputs" + ] + }, + { + "cell_type": "markdown", + "id": "8d3f905b-543d-4f32-b0dd-67ed99f77780", + "metadata": {}, + "source": [ + "**If you have already read any of the other parametric simulation examples, you can skip this entire outputs section, since it is exactly the same.**" + ] + }, + { + "cell_type": "markdown", + "id": "bc714539-79c3-46e9-9caa-44a21fc413cb", + "metadata": {}, + "source": [ + "### Outputs for the idf (i.e. the outputs for each simulation run)" + ] + }, + { + "cell_type": "markdown", + "id": "764b7626-0e63-4317-8bbf-28ac2fe68f68", + "metadata": {}, + "source": [ + "First of all, we are going to set the outputs of the simulations that are going to be performed. This is an important step, especially if you are going to run hundreds or thousands of simulations." + ] + }, + { + "cell_type": "markdown", + "id": "b2f1d34a-dc0a-4aac-b01f-a7425b95090f", + "metadata": {}, + "source": [ + "Let's take a look at the Output:Variable objects we currently have in the idf. The method `get_output_var_df_from_idf()` returns a pandas DataFrame which contains the information of the existing Output:Variable objects in the idf:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "12ee78ba-c60b-4ef1-a6fb-51086a8a53fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1*Adaptive Cooling Setpoint TemperatureHourly
2*Adaptive Heating Setpoint TemperatureHourly
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
...............
72*VRF Heat Pump Heating Electricity EnergyHourly
73BLOCK1_ZONE2 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
74BLOCK1_ZONE2 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
75BLOCK1_ZONE1 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
76BLOCK1_ZONE1 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
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" + ], + "text/plain": [ + " key_value \\\n", + "0 * \n", + "1 * \n", + "2 * \n", + "3 * \n", + "4 * \n", + ".. ... \n", + "72 * \n", + "73 BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil \n", + "74 BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil \n", + "75 BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil \n", + "76 BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil \n", + "\n", + " variable_name reporting_frequency \\\n", + "0 Comfort Temperature Hourly \n", + "1 Adaptive Cooling Setpoint Temperature Hourly \n", + "2 Adaptive Heating Setpoint Temperature Hourly \n", + "3 Adaptive Cooling Setpoint Temperature_No Toler... Hourly \n", + "4 Adaptive Heating Setpoint Temperature_No Toler... Hourly \n", + ".. ... ... \n", + "72 VRF Heat Pump Heating Electricity Energy Hourly \n", + "73 Cooling Coil Total Cooling Rate Hourly \n", + "74 Heating Coil Heating Rate Hourly \n", + "75 Cooling Coil Total Cooling Rate Hourly \n", + "76 Heating Coil Heating Rate Hourly \n", + "\n", + " schedule_name \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + ".. ... \n", + "72 \n", + "73 \n", + "74 \n", + "75 \n", + "76 \n", + "\n", + "[77 rows x 4 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = parametric.get_output_var_df_from_idf()\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "ab1e81e8-c2f0-40c0-b868-0cf7ddb9954f", + "metadata": {}, + "source": [ + "now, let's see the Output:Meter objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "94c9844b-aacf-41fa-b1d4-ac7b491aa14e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [key_name, frequency]\n", + "Index: []" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "093bdcab-a1aa-492a-8864-d2e4e1a0b72c", + "metadata": {}, + "source": [ + "In this case, we can see there is no Output:Meter. However, there is a large number of Output:Variable objects which might result in heavy simulation outputs. So, let's get rid of some of them. We can drop the rows we want, and then input the modified DataFrame in the method `set_output_var_df_to_idf(outputs_df)`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9ccc7b08-119c-4f4c-bb45-9aac8201fe7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "3 * Adaptive Cooling Setpoint Temperature_No Toler... \n", + "4 * Adaptive Heating Setpoint Temperature_No Toler... \n", + "52 * Zone Operative Temperature \n", + "54 * Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " reporting_frequency schedule_name \n", + "3 Hourly \n", + "4 Hourly \n", + "52 Hourly \n", + "54 Hourly " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = df_output_variables_idf[\n", + " (\n", + " df_output_variables_idf['variable_name'].str.contains('Setpoint Temperature_No Tolerance')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Operative Temperature')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')\n", + " )\n", + "]\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "643151b4-a20d-4d00-a1c9-5a78a56be916", + "metadata": {}, + "source": [ + "Let's keep only the Output:Variable objects we have filtered using the `set_output_var_df_to_idf(outputs_df)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8958bc66-f42d-4fc0-9622-f2b3ffb03852", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf)" + ] + }, + { + "cell_type": "markdown", + "id": "eacd9d6d-7fa1-4e6d-8bab-edbfa322140b", + "metadata": {}, + "source": [ + "We have removed all rows except the adaptive heating and cooling setpoints, the operative temperature and the running mean outdoor temperature. Next optional step is adding Output:Meter objects. We can do that using the method `set_output_met_objects_to_idf(output_meters)`, where `output_meters` is a list of Output:Meter key names." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "addf36d3-b7c2-4254-bec5-322850a464d1", + "metadata": {}, + "outputs": [], + "source": [ + "output_meters = [\n", + " 'Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + "]\n", + "parametric.set_output_met_objects_to_idf(output_meters=output_meters)" + ] + }, + { + "cell_type": "markdown", + "id": "fa66a111-e72e-4ebd-8110-8222191d6c5d", + "metadata": {}, + "source": [ + "Let's see Output:Meter objects we currently have after adding these:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "41878d11-6dcc-4158-9ec4-a74aa9d2dafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
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1Cooling:Electricityhourly
2Electricity:HVAChourly
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" + ], + "text/plain": [ + " key_name frequency\n", + "0 Heating:Electricity hourly\n", + "1 Cooling:Electricity hourly\n", + "2 Electricity:HVAC hourly" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "90fd8d9b-ecd8-4c79-9f37-775f2c2921ed", + "metadata": {}, + "source": [ + "### Outputs to be read and shown in the parametric simulation or optimisation" + ] + }, + { + "cell_type": "markdown", + "id": "bb18fd43-e07e-42f1-ac75-020bcbe96d9a", + "metadata": {}, + "source": [ + "To successfully run the parametric simulation or optimisation, it is advisable running a test simulation to know the outputs that each simulation will have. We can do that with the method `get_outputs_df_from_testsim()`, which returns a tuple containing 2 DataFrames containing respectively the Output:Meter and Output:Variable objects from the simulation. In this case, you won't find wildcards such as \"*\"." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ff6093e9-840d-4142-ad79-98d78f4f13d1", + "metadata": {}, + "outputs": [], + "source": [ + "df_output_meters_testsim, df_output_variables_testsim = parametric.get_outputs_df_from_testsim()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "61a88134-7997-42ce-88b0-ef4606cdfe61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1Cooling:ElectricityHourly
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1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly
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3BLOCK1:ZONE1Zone Operative TemperatureHourly
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5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "2 BLOCK1:ZONE2 Zone Operative Temperature \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "4 PEOPLE BLOCK1:ZONE2 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency \n", + "0 Hourly \n", + "1 Hourly \n", + "2 Hourly \n", + "3 Hourly \n", + "4 Hourly \n", + "5 Hourly " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_testsim" + ] + }, + { + "cell_type": "markdown", + "id": "b0ed4500-6054-4a00-ae63-54412bf70944", + "metadata": {}, + "source": [ + "We can get DataFrames from the .rdd and .mdd files generated from the test simulation using the functions `get_rdd_file_as_df()` and `get_mdd_file_as_df()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e28454a5-8de7-4551-bf9b-4db3fa8f124f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectkey_valuevariable_namefrequencyunits
0Output:Variable*Site Outdoor Air Drybulb Temperaturehourly!- Zone Average [C]
1Output:Variable*Site Outdoor Air Dewpoint Temperaturehourly!- Zone Average [C]
2Output:Variable*Site Outdoor Air Wetbulb Temperaturehourly!- Zone Average [C]
3Output:Variable*Site Outdoor Air Humidity Ratiohourly!- Zone Average [kgWater/kgDryAir]
4Output:Variable*Site Outdoor Air Relative Humidityhourly!- Zone Average [%]
..................
712Output:Variable*Zone Ventilation When Unoccupied Timehourly!- HVAC Sum [hr]
713Output:Variable*Facility Any Zone Ventilation Below Target Voz...hourly!- HVAC Sum [hr]
714Output:Variable*Facility All Zones Ventilation At Target Voz Timehourly!- HVAC Sum [hr]
715Output:Variable*Facility Any Zone Ventilation Above Target Voz...hourly!- HVAC Sum [hr]
716Output:Variable*Facility Any Zone Ventilation When Unoccupied ...hourly!- HVAC Sum [hr]
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717 rows × 5 columns

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" + ], + "text/plain": [ + " object key_value \\\n", + "0 Output:Variable * \n", + "1 Output:Variable * \n", + "2 Output:Variable * \n", + "3 Output:Variable * \n", + "4 Output:Variable * \n", + ".. ... ... \n", + "712 Output:Variable * \n", + "713 Output:Variable * \n", + "714 Output:Variable * \n", + "715 Output:Variable * \n", + "716 Output:Variable * \n", + "\n", + " variable_name frequency \\\n", + "0 Site Outdoor Air Drybulb Temperature hourly \n", + "1 Site Outdoor Air Dewpoint Temperature hourly \n", + "2 Site Outdoor Air Wetbulb Temperature hourly \n", + "3 Site Outdoor Air Humidity Ratio hourly \n", + "4 Site Outdoor Air Relative Humidity hourly \n", + ".. ... ... \n", + "712 Zone Ventilation When Unoccupied Time hourly \n", + "713 Facility Any Zone Ventilation Below Target Voz... hourly \n", + "714 Facility All Zones Ventilation At Target Voz Time hourly \n", + "715 Facility Any Zone Ventilation Above Target Voz... hourly \n", + "716 Facility Any Zone Ventilation When Unoccupied ... hourly \n", + "\n", + " units \n", + "0 !- Zone Average [C] \n", + "1 !- Zone Average [C] \n", + "2 !- Zone Average [C] \n", + "3 !- Zone Average [kgWater/kgDryAir] \n", + "4 !- Zone Average [%] \n", + ".. ... \n", + "712 !- HVAC Sum [hr] \n", + "713 !- HVAC Sum [hr] \n", + "714 !- HVAC Sum [hr] \n", + "715 !- HVAC Sum [hr] \n", + "716 !- HVAC Sum [hr] \n", + "\n", + "[717 rows x 5 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rdd = get_rdd_file_as_df()\n", + "df_rdd" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "58649f6e-f08c-49e5-823a-376ff26eb83a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectmeter_namefrequencyunits
0Output:MeterElectricity:Facilityhourly!- [J]
1Output:Meter:CumulativeElectricity:Facilityhourly!- [J]
2Output:MeterElectricity:Buildinghourly!- [J]
3Output:Meter:CumulativeElectricity:Buildinghourly!- [J]
4Output:MeterElectricity:Zone:BLOCK1:ZONE2hourly!- [J]
...............
157Output:Meter:CumulativeGeneral:HeatRecovery:EnergyTransferhourly!- [J]
158Output:MeterCarbon Equivalent:Facilityhourly!- [kg]
159Output:Meter:CumulativeCarbon Equivalent:Facilityhourly!- [kg]
160Output:MeterCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
161Output:Meter:CumulativeCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
\n", + "

162 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " object meter_name \\\n", + "0 Output:Meter Electricity:Facility \n", + "1 Output:Meter:Cumulative Electricity:Facility \n", + "2 Output:Meter Electricity:Building \n", + "3 Output:Meter:Cumulative Electricity:Building \n", + "4 Output:Meter Electricity:Zone:BLOCK1:ZONE2 \n", + ".. ... ... \n", + "157 Output:Meter:Cumulative General:HeatRecovery:EnergyTransfer \n", + "158 Output:Meter Carbon Equivalent:Facility \n", + "159 Output:Meter:Cumulative Carbon Equivalent:Facility \n", + "160 Output:Meter CarbonEquivalentEmissions:Carbon Equivalent \n", + "161 Output:Meter:Cumulative CarbonEquivalentEmissions:Carbon Equivalent \n", + "\n", + " frequency units \n", + "0 hourly !- [J] \n", + "1 hourly !- [J] \n", + "2 hourly !- [J] \n", + "3 hourly !- [J] \n", + "4 hourly !- [J] \n", + ".. ... ... \n", + "157 hourly !- [J] \n", + "158 hourly !- [kg] \n", + "159 hourly !- [kg] \n", + "160 hourly !- [kg] \n", + "161 hourly !- [kg] \n", + "\n", + "[162 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mdd = get_mdd_file_as_df()\n", + "df_mdd" + ] + }, + { + "cell_type": "markdown", + "id": "a84943b5-db09-4f19-95bb-f9583711d0a7", + "metadata": {}, + "source": [ + "Also, we can parse the .mtd files as a list using the function `parse_mtd_file()`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df4da1a7-fcb2-4c59-b790-1bc9e3b128f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'meter_id': '14',\n", + " 'description': 'BLOCK1:ZONE2 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']},\n", + " {'meter_id': '135',\n", + " 'description': 'BLOCK1:ZONE1 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']}]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mtd_list = parse_mtd_file()\n", + "mtd_list[0:2]" + ] + }, + { + "cell_type": "markdown", + "id": "76fa398d-15a0-4504-9f11-468bfd7c094f", + "metadata": {}, + "source": [ + "Therefore, we have 2 DataFrames, one for the Output:Meter and another for the Output:Variable objects. Next step is setting the outputs for the parametric simulation. To do so, we'll need to pass the DataFrames into the method `set_outputs_for_simulation(df_output_meter, df_output_variable)`. If you have some knowledge about the python package besos, you might think of these dataframes as if each row was a `MeterReader` or `VariableReader` instances respectively for the Output:Meter and Output:Variable dataframes, and the arguments in these were the specified in the columns. The `MeterReader` class takes the arguments `key_name`, `frequency`, `name` and `func`, while `VariableReader` class takes the arguments `key_value`, `variable_name`, `frequency`, `name` and `func`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b133883-a0be-42a9-bc04-0e6054337b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_name', 'frequency']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_meters_testsim.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c43b0496-1d36-4a0e-b2dc-9f77e41627ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_value', 'variable_name', 'frequency']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_variables_testsim.columns]" + ] + }, + { + "cell_type": "markdown", + "id": "a03bd526-8bdf-4cce-af62-73ac36e9d0c3", + "metadata": {}, + "source": [ + "If you take a look at the columns of the dataframes above, you can see the names are the arguments in the `MeterReader` and `VariableReader` classes, and only `name` and `func` are missing. That means, you can add these columns to input the `name` and `func` arguments as desired. In case of the Output:Meter dataframe, we won't add the `name` and `func` columns, which means the name will be the `key_name` and hourly results will be aggregated using the pd.Series.sum() function. However, in case of the Output:Variable dataframe, we will specify these: we want the hourly values rather than the aggregation, therefore we will pass the name bound to the function `return_time_series`, and we will add '_time series' as a suffix to the `variable_name` column. We will also remove the outputs for BLOCK1:ZONE2, which are the rows 2 and 4." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0e8fecdc-4625-4d95-a87d-67071884963b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namefrequencyfuncname
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x0000013FBD78...Adaptive Cooling Setpoint Temperature_No Toler...
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x0000013FBD78...Adaptive Heating Setpoint Temperature_No Toler...
3BLOCK1:ZONE1Zone Operative TemperatureHourly<function return_time_series at 0x0000013FBD78...Zone Operative Temperature_time series
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly<function return_time_series at 0x0000013FBD78...Zone Thermal Comfort ASHRAE 55 Adaptive Model ...
\n", + "
" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency func \\\n", + "0 Hourly , key_name='Heating:Electricity'),\n", + " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", + " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", + " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", + " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.sim_outputs" + ] + }, + { + "cell_type": "markdown", + "id": "b5dd98bb-6b1d-4b3c-b762-3c9a0c015f63", + "metadata": {}, + "source": [ + "## Setting the parameters" + ] + }, + { + "cell_type": "markdown", + "id": "4804beed-52e5-432a-a999-685c222d668d", + "metadata": {}, + "source": [ + "At the top of the script, when you instantiated the class `OptimParamSimulation`, you already specified which type of parameters you were going to use. Now, the parameters we're about to set, must match the `parameters_type` argument. At this point, you may not know which parameters you can use, so you can call the method `get_available_parameters()`, which will return a list of available parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "004029fa-ff72-4f43-a9c3-e004757463e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CustAST_ACSTaul',\n", + " 'CustAST_ACSTall',\n", + " 'CustAST_AHSTaul',\n", + " 'CustAST_AHSTall',\n", + " 'CustAST_ASTaul',\n", + " 'CustAST_ASTall',\n", + " 'CustAST_m',\n", + " 'CustAST_n',\n", + " 'CustAST_ACSToffset',\n", + " 'CustAST_AHSToffset',\n", + " 'CustAST_ASToffset']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "available_parameters = parametric.get_available_parameters()\n", + "available_parameters" + ] + }, + { + "cell_type": "markdown", + "id": "45886126-30b8-4e53-b8c0-06135e6a3c38", + "metadata": {}, + "source": [ + "If you don't know what are these, please refer to the [documentation](https://accim.readthedocs.io/en/master/4_detailed%20use.html).\n", + "\n", + "Using the 'accim custom model' type, the values can be either a list of options or a range of values. Now, let's set the parameters using the method `set_parameters(accis_params_dict, additional_params)`. In this method, we set the parameters related to accim using the argument `accis_params_dict`, which takes a dictionary following the pattern {'parameter name': [1, 2, 3, etc]} in case of list of options, or {'parameter name': (min_value, max_value)} in case of the range of values. We can also add some other parameters not related to accim in the argument `additional_params`, which takes a list of parameters as if these were input straight to the besos EPProblem class." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b4eb43dd-879b-4175-ab56-e9c04ea342af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_m.The name used as an input (CustAST_m) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_n.The name used as an input (CustAST_n) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_ASToffset.The name used as an input (CustAST_ASToffset) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_ASTall.The name used as an input (CustAST_ASTall) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CustAST_ASTaul.The name used as an input (CustAST_ASTaul) will be discarded.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "accis_parameters = {\n", + " 'CustAST_m': (0.01, 0.99),\n", + " 'CustAST_n': (5, 23),\n", + " 'CustAST_ASToffset': (2, 4),\n", + " 'CustAST_ASTall': (10, 15),\n", + " 'CustAST_ASTaul': (30, 35),\n", + "}\n", + "\n", + "parametric.set_parameters(accis_params_dict=accis_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "077a85a9-4493-4f7f-b3f6-e305dc9eb136", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetInputData, !- Name\n", + " set ComfStand = 99, !- Program Line 1\n", + " set CAT = 80, !- Program Line 2\n", + " set ComfMod = 3, !- Program Line 3\n", + " set HVACmode = 2, !- Program Line 4\n", + " set VentCtrl = 0, !- Program Line 5\n", + " set VSToffset = 0, !- Program Line 6\n", + " set MinOToffset = 50, !- Program Line 7\n", + " set MaxWindSpeed = 50, !- Program Line 8\n", + " set ACSTtol = -0.1, !- Program Line 9\n", + " set AHSTtol = 0.1, !- Program Line 10\n", + " set CoolSeasonStart = 121, !- Program Line 11\n", + " set CoolSeasonEnd = 274; !- Program Line 12]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setinputdata']" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "af64d963-4114-4bde-8c74-70f8b825fbe9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " ApplyCAT, !- Name\n", + " set CATcoolOffset = 0, !- Program Line 1\n", + " set CATheatOffset = 0, !- Program Line 2\n", + " if ComfStand == 99, !- Program Line 3\n", + " set ACSToffset = 0 + 0, !- Program Line 4\n", + " set AHSToffset = 0 + 0, !- Program Line 5\n", + " elseif (ComfStand == 1 ), !- Program Line 6\n", + " if (CAT == 1), !- Program Line 7\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 8\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 9\n", + " elseif (CAT == 2), !- Program Line 10\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 11\n", + " set AHSToffset = -4+CATheatOffset, !- Program Line 12\n", + " elseif (CAT == 3), !- Program Line 13\n", + " set ACSToffset = 4+CATcoolOffset, !- Program Line 14\n", + " set AHSToffset = -5+CATheatOffset, !- Program Line 15\n", + " endif, !- Program Line 16\n", + " elseif ComfStand == 2 || ComfStand == 3 || ComfStand == 11, !- Program Line 17\n", + " if (CAT == 90), !- Program Line 18\n", + " set ACSToffset = 2.5+CATcoolOffset, !- Program Line 19\n", + " set AHSToffset = -2.5+CATheatOffset, !- Program Line 20\n", + " elseif (CAT == 80), !- Program Line 21\n", + " set ACSToffset = 3.5+CATcoolOffset, !- Program Line 22\n", + " set AHSToffset = -3.5+CATheatOffset, !- Program Line 23\n", + " endif, !- Program Line 24\n", + " elseif (ComfStand == 4 ) || (ComfStand == 5) || (ComfStand == 6), !- Program Line 25\n", + " set ACSToffset = 0+CATcoolOffset, !- Program Line 26\n", + " set AHSToffset = 0+CATheatOffset, !- Program Line 27\n", + " elseif (ComfStand == 7), !- Program Line 28\n", + " if (CAT == 90), !- Program Line 29\n", + " set ACSToffset = 2.4+CATcoolOffset, !- Program Line 30\n", + " set AHSToffset = -2.4+CATheatOffset, !- Program Line 31\n", + " elseif (CAT == 85), !- Program Line 32\n", + " set ACSToffset = 3.3+CATcoolOffset, !- Program Line 33\n", + " set AHSToffset = -3.3+CATheatOffset, !- Program Line 34\n", + " elseif (CAT == 80), !- Program Line 35\n", + " set ACSToffset = 4.1+CATcoolOffset, !- Program Line 36\n", + " set AHSToffset = -4.1+CATheatOffset, !- Program Line 37\n", + " endif, !- Program Line 38\n", + " elseif (ComfStand == 8), !- Program Line 39\n", + " if (CAT == 90), !- Program Line 40\n", + " set ACSToffset = 3.5+CATcoolOffset, !- Program Line 41\n", + " set AHSToffset = -3.5+CATheatOffset, !- Program Line 42\n", + " elseif (CAT == 85), !- Program Line 43\n", + " set ACSToffset = 4.8+CATcoolOffset, !- Program Line 44\n", + " set AHSToffset = -4.8+CATheatOffset, !- Program Line 45\n", + " elseif (CAT == 80), !- Program Line 46\n", + " set ACSToffset = 5.9+CATcoolOffset, !- Program Line 47\n", + " set AHSToffset = -5.9+CATheatOffset, !- Program Line 48\n", + " endif, !- Program Line 49\n", + " elseif ComfStand == 9 || ComfStand == 10, !- Program Line 50\n", + " if (CAT == 90), !- Program Line 51\n", + " set ACSToffset = 2.15+CATcoolOffset, !- Program Line 52\n", + " set AHSToffset = -2.15+CATheatOffset, !- Program Line 53\n", + " elseif (CAT == 80), !- Program Line 54\n", + " set ACSToffset = 3.6+CATcoolOffset, !- Program Line 55\n", + " set AHSToffset = -3.6+CATheatOffset, !- Program Line 56\n", + " endif, !- Program Line 57\n", + " elseif ComfStand == 12, !- Program Line 58\n", + " if (CAT == 90), !- Program Line 59\n", + " set ACSToffset = 1.7+CATcoolOffset, !- Program Line 60\n", + " set AHSToffset = -1.7+CATheatOffset, !- Program Line 61\n", + " elseif (CAT == 80), !- Program Line 62\n", + " set ACSToffset = 2.89+CATcoolOffset, !- Program Line 63\n", + " set AHSToffset = -2.89+CATheatOffset, !- Program Line 64\n", + " endif, !- Program Line 65\n", + " elseif ComfStand == 13, !- Program Line 66\n", + " if (CAT == 90), !- Program Line 67\n", + " set ACSToffset = 3.45+CATcoolOffset, !- Program Line 68\n", + " set AHSToffset = -3.45+CATheatOffset, !- Program Line 69\n", + " elseif (CAT == 80), !- Program Line 70\n", + " set ACSToffset = 4.55+CATcoolOffset, !- Program Line 71\n", + " set AHSToffset = -4.55+CATheatOffset, !- Program Line 72\n", + " endif, !- Program Line 73\n", + " elseif ComfStand == 14, !- Program Line 74\n", + " if (CAT == 90), !- Program Line 75\n", + " set ACSToffset = 3.5+CATcoolOffset, !- Program Line 76\n", + " set AHSToffset = -3.5+CATheatOffset, !- Program Line 77\n", + " elseif (CAT == 80), !- Program Line 78\n", + " set ACSToffset = 4.5+CATcoolOffset, !- Program Line 79\n", + " set AHSToffset = -4.5+CATheatOffset, !- Program Line 80\n", + " endif, !- Program Line 81\n", + " elseif ComfStand == 15, !- Program Line 82\n", + " if (CAT == 90), !- Program Line 83\n", + " set ACSToffset = 2.8+CATcoolOffset, !- Program Line 84\n", + " set AHSToffset = -2.8+CATheatOffset, !- Program Line 85\n", + " elseif (CAT == 80), !- Program Line 86\n", + " set ACSToffset = 3.8+CATcoolOffset, !- Program Line 87\n", + " set AHSToffset = -3.8+CATheatOffset, !- Program Line 88\n", + " endif, !- Program Line 89\n", + " elseif ComfStand == 16, !- Program Line 90\n", + " if (CAT == 90), !- Program Line 91\n", + " set ACSToffset = 1.1+CATcoolOffset, !- Program Line 92\n", + " set AHSToffset = -1.1+CATheatOffset, !- Program Line 93\n", + " elseif (CAT == 80), !- Program Line 94\n", + " set ACSToffset = 2.1+CATcoolOffset, !- Program Line 95\n", + " set AHSToffset = -2.1+CATheatOffset, !- Program Line 96\n", + " endif, !- Program Line 97\n", + " elseif (ComfStand == 17) || (ComfStand == 18), !- Program Line 98\n", + " if CAT == 90, !- Program Line 99\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 100\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 101\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 102\n", + " else, !- Program Line 103\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 104\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 105\n", + " endif, !- Program Line 106\n", + " elseif CAT == 80, !- Program Line 107\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 108\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 109\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 110\n", + " else, !- Program Line 111\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 112\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 113\n", + " endif, !- Program Line 114\n", + " endif, !- Program Line 115\n", + " elseif ComfStand == 19, !- Program Line 116\n", + " if CAT == 90, !- Program Line 117\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 118\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 119\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 120\n", + " else, !- Program Line 121\n", + " set ACSToffset = 1+CATcoolOffset, !- Program Line 122\n", + " set AHSToffset = -1+CATheatOffset, !- Program Line 123\n", + " endif, !- Program Line 124\n", + " elseif CAT == 80, !- Program Line 125\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 126\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 127\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 128\n", + " else, !- Program Line 129\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 130\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 131\n", + " endif, !- Program Line 132\n", + " endif, !- Program Line 133\n", + " elseif ComfStand == 20, !- Program Line 134\n", + " if CAT == 90, !- Program Line 135\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 136\n", + " set ACSToffset = 2+CATcoolOffset, !- Program Line 137\n", + " set AHSToffset = -2+CATheatOffset, !- Program Line 138\n", + " else, !- Program Line 139\n", + " set ACSToffset = 5+CATcoolOffset, !- Program Line 140\n", + " set AHSToffset = -5+CATheatOffset, !- Program Line 141\n", + " endif, !- Program Line 142\n", + " elseif CAT == 80, !- Program Line 143\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 144\n", + " set ACSToffset = 3+CATcoolOffset, !- Program Line 145\n", + " set AHSToffset = -3+CATheatOffset, !- Program Line 146\n", + " else, !- Program Line 147\n", + " set ACSToffset = 6+CATcoolOffset, !- Program Line 148\n", + " set AHSToffset = -6+CATheatOffset, !- Program Line 149\n", + " endif, !- Program Line 150\n", + " endif, !- Program Line 151\n", + " elseif ComfStand == 21, !- Program Line 152\n", + " if (CAT == 90), !- Program Line 153\n", + " set ACSToffset = 2.5+CATcoolOffset, !- Program Line 154\n", + " set AHSToffset = -2.5+CATheatOffset, !- Program Line 155\n", + " elseif (CAT == 80), !- Program Line 156\n", + " set ACSToffset = 4+CATcoolOffset, !- Program Line 157\n", + " set AHSToffset = -4+CATheatOffset, !- Program Line 158\n", + " endif, !- Program Line 159\n", + " endif; !- Program Line 160]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'applycat']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "35cfc985-91fd-4b66-ad7e-076319dbe5e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetAST, !- Name\n", + " set SetpointAcc = 10000, !- Program Line 1\n", + " set m = 0, !- Program Line 2\n", + " set n = 0, !- Program Line 3\n", + " if CoolSeasonEnd > CoolSeasonStart, !- Program Line 4\n", + " if (DayOfYear >= CoolSeasonStart) && (DayOfYear < CoolSeasonEnd), !- Program Line 5\n", + " set CoolingSeason = 1, !- Program Line 6\n", + " else, !- Program Line 7\n", + " set CoolingSeason = 0, !- Program Line 8\n", + " endif, !- Program Line 9\n", + " elseif CoolSeasonStart > CoolSeasonEnd, !- Program Line 10\n", + " if (DayOfYear >= CoolSeasonStart) || (DayOfYear < CoolSeasonEnd), !- Program Line 11\n", + " set CoolingSeason = 1, !- Program Line 12\n", + " else, !- Program Line 13\n", + " set CoolingSeason = 0, !- Program Line 14\n", + " endif, !- Program Line 15\n", + " endif, !- Program Line 16\n", + " if (ComfStand == 99) && (ComfMod == 3), !- Program Line 17\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 18\n", + " set ACST = PMOT*m+n+ACSToffset+ACSTtol, !- Program Line 19\n", + " elseif PMOT < ACSTall, !- Program Line 20\n", + " set ACST = ACSTall*m+n+ACSToffset+ACSTtol, !- Program Line 21\n", + " elseif PMOT > ACSTaul, !- Program Line 22\n", + " set ACST = ACSTaul*m+n+ACSToffset+ACSTtol, !- Program Line 23\n", + " endif, !- Program Line 24\n", + " endif, !- Program Line 25\n", + " if (ComfStand == 99) && (ComfMod == 3), !- Program Line 26\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 27\n", + " set AHST = PMOT*m+n+AHSToffset+AHSTtol, !- Program Line 28\n", + " elseif PMOT < AHSTall, !- Program Line 29\n", + " set AHST = AHSTall*m+n+AHSToffset+AHSTtol, !- Program Line 30\n", + " elseif PMOT > AHSTaul, !- Program Line 31\n", + " set AHST = AHSTaul*m+n+AHSToffset+AHSTtol, !- Program Line 32\n", + " endif, !- Program Line 33\n", + " endif, !- Program Line 34\n", + " if (ComfStand == 0) && (CurrentTime < 8), !- Program Line 35\n", + " set ACST = 27+ACSTtol, !- Program Line 36\n", + " set AHST = 17+AHSTtol, !- Program Line 37\n", + " elseif (ComfStand == 0) && (CurrentTime < 16), !- Program Line 38\n", + " set ACST = 25+ACSTtol, !- Program Line 39\n", + " set AHST = 20+AHSTtol, !- Program Line 40\n", + " elseif (ComfStand == 0) && (CurrentTime < 23), !- Program Line 41\n", + " set ACST = 25+ACSTtol, !- Program Line 42\n", + " set AHST = 20+AHSTtol, !- Program Line 43\n", + " elseif (ComfStand == 0) && (CurrentTime < 24), !- Program Line 44\n", + " set ACST = 27+ACSTtol, !- Program Line 45\n", + " set AHST = 17+AHSTtol, !- Program Line 46\n", + " endif, !- Program Line 47\n", + " if (ComfStand == 1) && (ComfMod == 0), !- Program Line 48\n", + " if CoolingSeason == 1, !- Program Line 49\n", + " if (CAT==1), !- Program Line 50\n", + " set ACST = 25.5+ACSTtol, !- Program Line 51\n", + " elseif (CAT==2), !- Program Line 52\n", + " set ACST = 26+ACSTtol, !- Program Line 53\n", + " elseif (CAT==3), !- Program Line 54\n", + " set ACST = 27+ACSTtol, !- Program Line 55\n", + " endif, !- Program Line 56\n", + " else, !- Program Line 57\n", + " if (CAT==1), !- Program Line 58\n", + " set ACST = 25+ACSTtol, !- Program Line 59\n", + " elseif (CAT==2), !- Program Line 60\n", + " set ACST = 25+ACSTtol, !- Program Line 61\n", + " elseif (CAT==3), !- Program Line 62\n", + " set ACST = 25+ACSTtol, !- Program Line 63\n", + " endif, !- Program Line 64\n", + " endif, !- Program Line 65\n", + " endif, !- Program Line 66\n", + " if (ComfStand == 1) && (ComfMod == 0), !- Program Line 67\n", + " if CoolingSeason == 1, !- Program Line 68\n", + " if (CAT==1), !- Program Line 69\n", + " set AHST = 23.5+AHSTtol, !- Program Line 70\n", + " elseif (CAT==2), !- Program Line 71\n", + " set AHST = 23+AHSTtol, !- Program Line 72\n", + " elseif (CAT==3), !- Program Line 73\n", + " set AHST = 22+AHSTtol, !- Program Line 74\n", + " endif, !- Program Line 75\n", + " else, !- Program Line 76\n", + " if (CAT==1), !- Program Line 77\n", + " set AHST = 21+AHSTtol, !- Program Line 78\n", + " elseif (CAT==2), !- Program Line 79\n", + " set AHST = 20+AHSTtol, !- Program Line 80\n", + " elseif (CAT==3), !- Program Line 81\n", + " set AHST = 18+AHSTtol, !- Program Line 82\n", + " endif, !- Program Line 83\n", + " endif, !- Program Line 84\n", + " endif, !- Program Line 85\n", + " if (ComfStand == 1) && (ComfMod == 1), !- Program Line 86\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 87\n", + " set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 88\n", + " elseif CurrentTime < 7, !- Program Line 89\n", + " set ACST = 27+ACSTtol, !- Program Line 90\n", + " elseif CurrentTime < 15, !- Program Line 91\n", + " set ACST = 50, !- Program Line 92\n", + " elseif CurrentTime < 23, !- Program Line 93\n", + " set ACST = 25+ACSTtol, !- Program Line 94\n", + " elseif CurrentTime < 24, !- Program Line 95\n", + " set ACST = 27+ACSTtol, !- Program Line 96\n", + " endif, !- Program Line 97\n", + " endif, !- Program Line 98\n", + " if (ComfStand == 1) && (ComfMod == 1), !- Program Line 99\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 100\n", + " set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 101\n", + " elseif CurrentTime < 7, !- Program Line 102\n", + " set AHST = 17+AHSTtol, !- Program Line 103\n", + " elseif CurrentTime < 23, !- Program Line 104\n", + " set AHST = 20+AHSTtol, !- Program Line 105\n", + " elseif CurrentTime < 24, !- Program Line 106\n", + " set AHST = 17+AHSTtol, !- Program Line 107\n", + " endif, !- Program Line 108\n", + " endif, !- Program Line 109\n", + " if (ComfStand == 1) && (ComfMod == 2), !- Program Line 110\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 111\n", + " set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 112\n", + " elseif (RMOT < ACSTall) && (CAT==1), !- Program Line 113\n", + " set ACST = 25+ACSTtol, !- Program Line 114\n", + " elseif (RMOT > ACSTaul) && (CAT==1), !- Program Line 115\n", + " set ACST = 25.5+ACSTtol, !- Program Line 116\n", + " elseif (RMOT < ACSTall) && (CAT==2), !- Program Line 117\n", + " set ACST = 25+ACSTtol, !- Program Line 118\n", + " elseif (RMOT > ACSTaul) && (CAT==2), !- Program Line 119\n", + " set ACST = 26+ACSTtol, !- Program Line 120\n", + " elseif (RMOT < ACSTall) && (CAT==3), !- Program Line 121\n", + " set ACST = 25+ACSTtol, !- Program Line 122\n", + " elseif (RMOT > ACSTaul) && (CAT==3), !- Program Line 123\n", + " set ACST = 27+ACSTtol, !- Program Line 124\n", + " endif, !- Program Line 125\n", + " endif, !- Program Line 126\n", + " if (ComfStand == 1) && (ComfMod == 2), !- Program Line 127\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 128\n", + " set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 129\n", + " elseif (RMOT < AHSTall) && (CAT==1), !- Program Line 130\n", + " set AHST = 21+AHSTtol, !- Program Line 131\n", + " elseif (RMOT > AHSTaul) && (CAT==1), !- Program Line 132\n", + " set AHST = 23.5+AHSTtol, !- Program Line 133\n", + " elseif (RMOT < AHSTall) && (CAT==2), !- Program Line 134\n", + " set AHST = 20+AHSTtol, !- Program Line 135\n", + " elseif (RMOT > AHSTaul) && (CAT==2), !- Program Line 136\n", + " set AHST = 23+AHSTtol, !- Program Line 137\n", + " elseif (RMOT < AHSTall) && (CAT==3), !- Program Line 138\n", + " set AHST = 18+AHSTtol, !- Program Line 139\n", + " elseif (RMOT > AHSTaul) && (CAT==3), !- Program Line 140\n", + " set AHST = 22+AHSTtol, !- Program Line 141\n", + " endif, !- Program Line 142\n", + " endif, !- Program Line 143\n", + " if (ComfStand == 1) && (ComfMod == 3), !- Program Line 144\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 145\n", + " set ACST = RMOT*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 146\n", + " elseif RMOT < ACSTall, !- Program Line 147\n", + " set ACST = ACSTall*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 148\n", + " elseif RMOT > ACSTaul, !- Program Line 149\n", + " set ACST = ACSTaul*0.33+18.8+ACSToffset+ACSTtol, !- Program Line 150\n", + " endif, !- Program Line 151\n", + " endif, !- Program Line 152\n", + " if (ComfStand == 1) && (ComfMod == 3), !- Program Line 153\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 154\n", + " set AHST = RMOT*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 155\n", + " elseif RMOT < AHSTall, !- Program Line 156\n", + " set AHST = AHSTall*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 157\n", + " elseif RMOT > AHSTaul, !- Program Line 158\n", + " set AHST = AHSTaul*0.33+18.8+AHSToffset+AHSTtol, !- Program Line 159\n", + " endif, !- Program Line 160\n", + " endif, !- Program Line 161\n", + " if (ComfStand == 2) && (ComfMod == 0), !- Program Line 162\n", + " if CoolingSeason == 1, !- Program Line 163\n", + " if (CAT==80), !- Program Line 164\n", + " set ACST = 27+ACSTtol, !- Program Line 165\n", + " elseif (CAT==90), !- Program Line 166\n", + " set ACST = 26+ACSTtol, !- Program Line 167\n", + " endif, !- Program Line 168\n", + " else, !- Program Line 169\n", + " if (CAT==80), !- Program Line 170\n", + " set ACST = 25+ACSTtol, !- Program Line 171\n", + " elseif (CAT==90), !- Program Line 172\n", + " set ACST = 24+ACSTtol, !- Program Line 173\n", + " endif, !- Program Line 174\n", + " endif, !- Program Line 175\n", + " endif, !- Program Line 176\n", + " if (ComfStand == 2) && (ComfMod == 0), !- Program Line 177\n", + " if CoolingSeason == 1, !- Program Line 178\n", + " if (CAT==80), !- Program Line 179\n", + " set AHST = 22+AHSTtol, !- Program Line 180\n", + " elseif (CAT==90), !- Program Line 181\n", + " set AHST = 23+AHSTtol, !- Program Line 182\n", + " endif, !- Program Line 183\n", + " else, !- Program Line 184\n", + " if (CAT==80), !- Program Line 185\n", + " set AHST = 19+AHSTtol, !- Program Line 186\n", + " elseif (CAT==90), !- Program Line 187\n", + " set AHST = 20+AHSTtol, !- Program Line 188\n", + " endif, !- Program Line 189\n", + " endif, !- Program Line 190\n", + " endif, !- Program Line 191\n", + " if (ComfStand == 2) && (ComfMod == 1), !- Program Line 192\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 193\n", + " set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 194\n", + " elseif CurrentTime < 7, !- Program Line 195\n", + " set ACST = 27+ACSTtol, !- Program Line 196\n", + " elseif CurrentTime < 15, !- Program Line 197\n", + " set ACST = 50, !- Program Line 198\n", + " elseif CurrentTime < 23, !- Program Line 199\n", + " set ACST = 25+ACSTtol, !- Program Line 200\n", + " elseif CurrentTime < 24, !- Program Line 201\n", + " set ACST = 27+ACSTtol, !- Program Line 202\n", + " endif, !- Program Line 203\n", + " endif, !- Program Line 204\n", + " if (ComfStand == 2) && (ComfMod == 1), !- Program Line 205\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 206\n", + " set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 207\n", + " elseif CurrentTime < 7, !- Program Line 208\n", + " set AHST = 17+AHSTtol, !- Program Line 209\n", + " elseif CurrentTime < 23, !- Program Line 210\n", + " set AHST = 20+AHSTtol, !- Program Line 211\n", + " elseif CurrentTime < 24, !- Program Line 212\n", + " set AHST = 17+AHSTtol, !- Program Line 213\n", + " endif, !- Program Line 214\n", + " endif, !- Program Line 215\n", + " if (ComfStand == 2) && (ComfMod == 2), !- Program Line 216\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 217\n", + " set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 218\n", + " elseif CAT==80, !- Program Line 219\n", + " if PMOT < ACSTall, !- Program Line 220\n", + " set ACST = 25+ACSTtol, !- Program Line 221\n", + " elseif PMOT > ACSTaul, !- Program Line 222\n", + " set ACST = 27+ACSTtol, !- Program Line 223\n", + " endif, !- Program Line 224\n", + " elseif CAT==90, !- Program Line 225\n", + " if PMOT < ACSTall, !- Program Line 226\n", + " set ACST = 24+ACSTtol, !- Program Line 227\n", + " elseif PMOT > ACSTaul, !- Program Line 228\n", + " set ACST = 26+ACSTtol, !- Program Line 229\n", + " endif, !- Program Line 230\n", + " endif, !- Program Line 231\n", + " endif, !- Program Line 232\n", + " if (ComfStand == 2) && (ComfMod == 2), !- Program Line 233\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 234\n", + " set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 235\n", + " elseif CAT==80, !- Program Line 236\n", + " if PMOT < AHSTall, !- Program Line 237\n", + " set AHST = 19+AHSTtol, !- Program Line 238\n", + " elseif PMOT > AHSTaul, !- Program Line 239\n", + " set AHST = 22+AHSTtol, !- Program Line 240\n", + " endif, !- Program Line 241\n", + " elseif CAT==90, !- Program Line 242\n", + " if PMOT < AHSTall, !- Program Line 243\n", + " set AHST = 20+AHSTtol, !- Program Line 244\n", + " elseif PMOT > AHSTaul, !- Program Line 245\n", + " set AHST = 23+AHSTtol, !- Program Line 246\n", + " endif, !- Program Line 247\n", + " endif, !- Program Line 248\n", + " endif, !- Program Line 249\n", + " if (ComfStand == 2) && (ComfMod == 3), !- Program Line 250\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 251\n", + " set ACST = PMOT*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 252\n", + " elseif PMOT < ACSTall, !- Program Line 253\n", + " set ACST = ACSTall*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 254\n", + " elseif PMOT > ACSTaul, !- Program Line 255\n", + " set ACST = ACSTaul*0.31+17.8+ACSToffset+ACSTtol, !- Program Line 256\n", + " endif, !- Program Line 257\n", + " endif, !- Program Line 258\n", + " if (ComfStand == 2) && (ComfMod == 3), !- Program Line 259\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 260\n", + " set AHST = PMOT*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 261\n", + " elseif PMOT < AHSTall, !- Program Line 262\n", + " set AHST = AHSTall*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 263\n", + " elseif PMOT > AHSTaul, !- Program Line 264\n", + " set AHST = AHSTaul*0.31+17.8+AHSToffset+AHSTtol, !- Program Line 265\n", + " endif, !- Program Line 266\n", + " endif, !- Program Line 267\n", + " if (ComfStand == 3) && (ComfMod == 0), !- Program Line 268\n", + " if (CAT==80), !- Program Line 269\n", + " set ACST = 28+ACSTtol, !- Program Line 270\n", + " elseif (CAT==90), !- Program Line 271\n", + " set ACST = 27+ACSTtol, !- Program Line 272\n", + " endif, !- Program Line 273\n", + " endif, !- Program Line 274\n", + " if (ComfStand == 3) && (ComfMod == 0), !- Program Line 275\n", + " if (CAT==80), !- Program Line 276\n", + " set AHST = 18+AHSTtol, !- Program Line 277\n", + " elseif (CAT==90), !- Program Line 278\n", + " set AHST = 19+AHSTtol, !- Program Line 279\n", + " endif, !- Program Line 280\n", + " endif, !- Program Line 281\n", + " if (ComfStand == 3) && (ComfMod == 1), !- Program Line 282\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 283\n", + " set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 284\n", + " elseif CAT==80, !- Program Line 285\n", + " if PMOT < ACSTall, !- Program Line 286\n", + " set ACST = 28+ACSTtol, !- Program Line 287\n", + " elseif PMOT > ACSTaul, !- Program Line 288\n", + " set ACST = 28+ACSTtol, !- Program Line 289\n", + " endif, !- Program Line 290\n", + " elseif CAT==90, !- Program Line 291\n", + " if PMOT < ACSTall, !- Program Line 292\n", + " set ACST = 27+ACSTtol, !- Program Line 293\n", + " elseif PMOT > ACSTaul, !- Program Line 294\n", + " set ACST = 27+ACSTtol, !- Program Line 295\n", + " endif, !- Program Line 296\n", + " endif, !- Program Line 297\n", + " endif, !- Program Line 298\n", + " if (ComfStand == 3) && (ComfMod == 1), !- Program Line 299\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 300\n", + " set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 301\n", + " elseif CAT==80, !- Program Line 302\n", + " if PMOT < AHSTall, !- Program Line 303\n", + " set AHST = 18+AHSTtol, !- Program Line 304\n", + " elseif PMOT > AHSTaul, !- Program Line 305\n", + " set AHST = 18+AHSTtol, !- Program Line 306\n", + " endif, !- Program Line 307\n", + " elseif CAT==90, !- Program Line 308\n", + " if PMOT < AHSTall, !- Program Line 309\n", + " set AHST = 19+AHSTtol, !- Program Line 310\n", + " elseif PMOT > AHSTaul, !- Program Line 311\n", + " set AHST = 19+AHSTtol, !- Program Line 312\n", + " endif, !- Program Line 313\n", + " endif, !- Program Line 314\n", + " endif, !- Program Line 315\n", + " if (ComfStand == 3) && (ComfMod == 2), !- Program Line 316\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 317\n", + " set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 318\n", + " elseif CAT==80, !- Program Line 319\n", + " if PMOT < ACSTall, !- Program Line 320\n", + " set ACST = 25+ACSTtol, !- Program Line 321\n", + " elseif PMOT > ACSTaul, !- Program Line 322\n", + " set ACST = 27+ACSTtol, !- Program Line 323\n", + " endif, !- Program Line 324\n", + " elseif CAT==90, !- Program Line 325\n", + " if PMOT < ACSTall, !- Program Line 326\n", + " set ACST = 24+ACSTtol, !- Program Line 327\n", + " elseif PMOT > ACSTaul, !- Program Line 328\n", + " set ACST = 26+ACSTtol, !- Program Line 329\n", + " endif, !- Program Line 330\n", + " endif, !- Program Line 331\n", + " endif, !- Program Line 332\n", + " if (ComfStand == 3) && (ComfMod == 2), !- Program Line 333\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 334\n", + " set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 335\n", + " elseif CAT==80, !- Program Line 336\n", + " if PMOT < AHSTall, !- Program Line 337\n", + " set AHST = 19+AHSTtol, !- Program Line 338\n", + " elseif PMOT > AHSTaul, !- Program Line 339\n", + " set AHST = 22+AHSTtol, !- Program Line 340\n", + " endif, !- Program Line 341\n", + " elseif CAT==90, !- Program Line 342\n", + " if PMOT < AHSTall, !- Program Line 343\n", + " set AHST = 20+AHSTtol, !- Program Line 344\n", + " elseif PMOT > AHSTaul, !- Program Line 345\n", + " set AHST = 23+AHSTtol, !- Program Line 346\n", + " endif, !- Program Line 347\n", + " endif, !- Program Line 348\n", + " endif, !- Program Line 349\n", + " if (ComfStand == 3) && (ComfMod == 3), !- Program Line 350\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 351\n", + " set ACST = PMOT*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 352\n", + " elseif PMOT < ACSTall, !- Program Line 353\n", + " set ACST = ACSTall*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 354\n", + " elseif PMOT > ACSTaul, !- Program Line 355\n", + " set ACST = ACSTaul*0.48+14.4+ACSToffset+ACSTtol, !- Program Line 356\n", + " endif, !- Program Line 357\n", + " endif, !- Program Line 358\n", + " if (ComfStand == 3) && (ComfMod == 3), !- Program Line 359\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 360\n", + " set AHST = PMOT*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 361\n", + " elseif PMOT < AHSTall, !- Program Line 362\n", + " set AHST = AHSTall*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 363\n", + " elseif PMOT > AHSTaul, !- Program Line 364\n", + " set AHST = AHSTaul*0.48+14.4+AHSToffset+AHSTtol, !- Program Line 365\n", + " endif, !- Program Line 366\n", + " endif, !- Program Line 367\n", + " if (ComfStand == 4) && (ComfMod == 3), !- Program Line 368\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 369\n", + " if CAT == 1, !- Program Line 370\n", + " set ACST = PMOT*0.77+12.04+ACSTtol, !- Program Line 371\n", + " elseif CAT == 2, !- Program Line 372\n", + " set ACST = PMOT*0.73+15.28+ACSTtol, !- Program Line 373\n", + " endif, !- Program Line 374\n", + " elseif PMOT < ACSTall, !- Program Line 375\n", + " if CAT == 1, !- Program Line 376\n", + " set ACST = ACSTall*0.77+12.04+ACSTtol, !- Program Line 377\n", + " elseif CAT == 2, !- Program Line 378\n", + " set ACST = ACSTall*0.73+15.28+ACSTtol, !- Program Line 379\n", + " endif, !- Program Line 380\n", + " elseif PMOT > ACSTaul, !- Program Line 381\n", + " if CAT == 1, !- Program Line 382\n", + " set ACST = ACSTaul*0.77+12.04+ACSTtol, !- Program Line 383\n", + " elseif CAT == 2, !- Program Line 384\n", + " set ACST = ACSTaul*0.73+15.28+ACSTtol, !- Program Line 385\n", + " endif, !- Program Line 386\n", + " endif, !- Program Line 387\n", + " endif, !- Program Line 388\n", + " if (ComfStand == 4) && (ComfMod == 3), !- Program Line 389\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 390\n", + " if CAT == 1, !- Program Line 391\n", + " set AHST = PMOT*0.87+2.76+AHSTtol, !- Program Line 392\n", + " elseif CAT == 2, !- Program Line 393\n", + " set AHST = PMOT*0.91-0.48+AHSTtol, !- Program Line 394\n", + " endif, !- Program Line 395\n", + " elseif PMOT < AHSTall, !- Program Line 396\n", + " if CAT == 1, !- Program Line 397\n", + " set AHST = AHSTall*0.87+2.76+AHSTtol, !- Program Line 398\n", + " elseif CAT == 2, !- Program Line 399\n", + " set AHST = AHSTall*0.91-0.48+AHSTtol, !- Program Line 400\n", + " endif, !- Program Line 401\n", + " elseif PMOT > AHSTaul, !- Program Line 402\n", + " if CAT == 1, !- Program Line 403\n", + " set AHST = AHSTaul*0.87+2.76+AHSTtol, !- Program Line 404\n", + " elseif CAT == 2, !- Program Line 405\n", + " set AHST = AHSTaul*0.91-0.48+AHSTtol, !- Program Line 406\n", + " endif, !- Program Line 407\n", + " endif, !- Program Line 408\n", + " endif, !- Program Line 409\n", + " if (ComfStand == 5) && (ComfMod == 3), !- Program Line 410\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 411\n", + " if CAT == 1, !- Program Line 412\n", + " set ACST = PMOT*0.77+9.34+ACSTtol, !- Program Line 413\n", + " elseif CAT == 2, !- Program Line 414\n", + " set ACST = PMOT*0.73+12.72+ACSTtol, !- Program Line 415\n", + " endif, !- Program Line 416\n", + " elseif PMOT < ACSTall, !- Program Line 417\n", + " if CAT == 1, !- Program Line 418\n", + " set ACST = ACSTall*0.77+9.34+ACSTtol, !- Program Line 419\n", + " elseif CAT == 2, !- Program Line 420\n", + " set ACST = ACSTall*0.73+12.72+ACSTtol, !- Program Line 421\n", + " endif, !- Program Line 422\n", + " elseif PMOT > ACSTaul, !- Program Line 423\n", + " if CAT == 1, !- Program Line 424\n", + " set ACST = ACSTaul*0.77+9.34+ACSTtol, !- Program Line 425\n", + " elseif CAT == 2, !- Program Line 426\n", + " set ACST = ACSTaul*0.73+12.72+ACSTtol, !- Program Line 427\n", + " endif, !- Program Line 428\n", + " endif, !- Program Line 429\n", + " endif, !- Program Line 430\n", + " if (ComfStand == 5) && (ComfMod == 3), !- Program Line 431\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 432\n", + " if CAT == 1, !- Program Line 433\n", + " set AHST = PMOT*0.87-0.31+AHSTtol, !- Program Line 434\n", + " elseif CAT == 2, !- Program Line 435\n", + " set AHST = PMOT*0.91-3.69+AHSTtol, !- Program Line 436\n", + " endif, !- Program Line 437\n", + " elseif PMOT < AHSTall, !- Program Line 438\n", + " if CAT == 1, !- Program Line 439\n", + " set AHST = AHSTall*0.87-0.31+AHSTtol, !- Program Line 440\n", + " elseif CAT == 2, !- Program Line 441\n", + " set AHST = AHSTall*0.91-3.69+AHSTtol, !- Program Line 442\n", + " endif, !- Program Line 443\n", + " elseif PMOT > AHSTaul, !- Program Line 444\n", + " if CAT == 1, !- Program Line 445\n", + " set AHST = AHSTaul*0.87-0.31+AHSTtol, !- Program Line 446\n", + " elseif CAT == 2, !- Program Line 447\n", + " set AHST = AHSTaul*0.91-3.69+AHSTtol, !- Program Line 448\n", + " endif, !- Program Line 449\n", + " endif, !- Program Line 450\n", + " endif, !- Program Line 451\n", + " if (ComfStand == 6) && (ComfMod == 2), !- Program Line 452\n", + " if CAT==80, !- Program Line 453\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 454\n", + " set ACST = PMOT*0.30+25.9+ACSTtol, !- Program Line 455\n", + " elseif PMOT < ACSTall, !- Program Line 456\n", + " set ACST = 25+ACSTtol, !- Program Line 457\n", + " elseif PMOT > ACSTaul, !- Program Line 458\n", + " set ACST = 27+ACSTtol, !- Program Line 459\n", + " endif, !- Program Line 460\n", + " elseif CAT==90, !- Program Line 461\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 462\n", + " set ACST = PMOT*0.30+23.6+ACSTtol, !- Program Line 463\n", + " elseif PMOT < ACSTall, !- Program Line 464\n", + " set ACST = 24+ACSTtol, !- Program Line 465\n", + " elseif PMOT > ACSTaul, !- Program Line 466\n", + " set ACST = 26+ACSTtol, !- Program Line 467\n", + " endif, !- Program Line 468\n", + " endif, !- Program Line 469\n", + " endif, !- Program Line 470\n", + " if (ComfStand == 6) && (ComfMod == 2), !- Program Line 471\n", + " if CAT==80, !- Program Line 472\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 473\n", + " set AHST = PMOT*0.30+25.9+AHSTtol, !- Program Line 474\n", + " elseif PMOT < AHSTall, !- Program Line 475\n", + " set AHST = 19+AHSTtol, !- Program Line 476\n", + " elseif PMOT > AHSTaul, !- Program Line 477\n", + " set AHST = 22+AHSTtol, !- Program Line 478\n", + " endif, !- Program Line 479\n", + " elseif CAT==90, !- Program Line 480\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 481\n", + " set AHST = PMOT*0.30+23.6+AHSTtol, !- Program Line 482\n", + " elseif PMOT < AHSTall, !- Program Line 483\n", + " set AHST = 20+AHSTtol, !- Program Line 484\n", + " elseif PMOT > AHSTaul, !- Program Line 485\n", + " set AHST = 23+AHSTtol, !- Program Line 486\n", + " endif, !- Program Line 487\n", + " endif, !- Program Line 488\n", + " endif, !- Program Line 489\n", + " if (ComfStand == 6) && (ComfMod == 3), !- Program Line 490\n", + " if CAT == 80, !- Program Line 491\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 492\n", + " set ACST = PMOT*0.30+25.9+ACSTtol, !- Program Line 493\n", + " elseif (PMOT < ACSTall), !- Program Line 494\n", + " set ACST = ACSTall*0.30+25.9+ACSTtol, !- Program Line 495\n", + " elseif (PMOT > ACSTaul), !- Program Line 496\n", + " set ACST = ACSTaul*0.30+25.9+ACSTtol, !- Program Line 497\n", + " endif, !- Program Line 498\n", + " elseif CAT == 90, !- Program Line 499\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 500\n", + " set ACST = PMOT*0.30+23.6+ACSTtol, !- Program Line 501\n", + " elseif (PMOT < ACSTall), !- Program Line 502\n", + " set ACST = ACSTall*0.30+23.6+ACSTtol, !- Program Line 503\n", + " elseif (PMOT > ACSTaul), !- Program Line 504\n", + " set ACST = ACSTaul*0.30+23.6+ACSTtol, !- Program Line 505\n", + " endif, !- Program Line 506\n", + " endif, !- Program Line 507\n", + " endif, !- Program Line 508\n", + " if (ComfStand == 6) && (ComfMod == 3), !- Program Line 509\n", + " if CAT == 80, !- Program Line 510\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 511\n", + " set AHST = PMOT*0.32+14.88+AHSTtol, !- Program Line 512\n", + " elseif (PMOT < AHSTall), !- Program Line 513\n", + " set AHST = AHSTall*0.32+14.88+AHSTtol, !- Program Line 514\n", + " elseif (PMOT > AHSTaul), !- Program Line 515\n", + " set AHST = AHSTaul*0.32+14.88+AHSTtol, !- Program Line 516\n", + " endif, !- Program Line 517\n", + " elseif CAT == 90, !- Program Line 518\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 519\n", + " set AHST = PMOT*0.31+17.14+AHSTtol, !- Program Line 520\n", + " elseif (PMOT < AHSTall), !- Program Line 521\n", + " set AHST = AHSTall*0.31+17.14+AHSTtol, !- Program Line 522\n", + " elseif (PMOT > AHSTaul), !- Program Line 523\n", + " set AHST = AHSTaul*0.31+17.14+AHSTtol, !- Program Line 524\n", + " endif, !- Program Line 525\n", + " endif, !- Program Line 526\n", + " endif, !- Program Line 527\n", + " if ComfStand == 7 || ComfStand == 8 || ComfStand == 9 || ComfStand == 10, !- Program Line 528\n", + " if (ComfMod == 0), !- Program Line 529\n", + " if CAT==80, !- Program Line 530\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 531\n", + " elseif CAT==85, !- Program Line 532\n", + " set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 533\n", + " elseif CAT==90, !- Program Line 534\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 535\n", + " endif, !- Program Line 536\n", + " endif, !- Program Line 537\n", + " endif, !- Program Line 538\n", + " if ComfStand == 7 || ComfStand == 8 || ComfStand == 9 || ComfStand == 10, !- Program Line 539\n", + " if (ComfMod == 0), !- Program Line 540\n", + " if CAT==80, !- Program Line 541\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 542\n", + " elseif CAT==85, !- Program Line 543\n", + " set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 544\n", + " elseif CAT==90, !- Program Line 545\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 546\n", + " endif, !- Program Line 547\n", + " endif, !- Program Line 548\n", + " endif, !- Program Line 549\n", + " if (ComfStand == 7) && (ComfMod == 1), !- Program Line 550\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 551\n", + " set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 552\n", + " else, !- Program Line 553\n", + " if CAT==80, !- Program Line 554\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 555\n", + " elseif CAT==85, !- Program Line 556\n", + " set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 557\n", + " elseif CAT==90, !- Program Line 558\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 559\n", + " endif, !- Program Line 560\n", + " endif, !- Program Line 561\n", + " endif, !- Program Line 562\n", + " if (ComfStand == 7) && (ComfMod == 1), !- Program Line 563\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 564\n", + " set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 565\n", + " else, !- Program Line 566\n", + " if CAT==80, !- Program Line 567\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 568\n", + " elseif CAT==85, !- Program Line 569\n", + " set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 570\n", + " elseif CAT==90, !- Program Line 571\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 572\n", + " endif, !- Program Line 573\n", + " endif, !- Program Line 574\n", + " endif, !- Program Line 575\n", + " if (ComfStand == 7) && (ComfMod == 2), !- Program Line 576\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 577\n", + " set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 578\n", + " elseif CAT==80, !- Program Line 579\n", + " if PMOT < ACSTall, !- Program Line 580\n", + " set ACST = 25+ACSTtol, !- Program Line 581\n", + " elseif PMOT > ACSTaul, !- Program Line 582\n", + " set ACST = 27+ACSTtol, !- Program Line 583\n", + " endif, !- Program Line 584\n", + " elseif CAT==85, !- Program Line 585\n", + " if PMOT < ACSTall, !- Program Line 586\n", + " set ACST = 25.72+ACSTtol, !- Program Line 587\n", + " elseif PMOT > ACSTaul, !- Program Line 588\n", + " set ACST = 27.89+ACSTtol, !- Program Line 589\n", + " endif, !- Program Line 590\n", + " elseif CAT==90, !- Program Line 591\n", + " if PMOT < ACSTall, !- Program Line 592\n", + " set ACST = 24+ACSTtol, !- Program Line 593\n", + " elseif PMOT > ACSTaul, !- Program Line 594\n", + " set ACST = 26+ACSTtol, !- Program Line 595\n", + " endif, !- Program Line 596\n", + " endif, !- Program Line 597\n", + " endif, !- Program Line 598\n", + " if (ComfStand == 7) && (ComfMod == 2), !- Program Line 599\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 600\n", + " set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 601\n", + " elseif CAT==80, !- Program Line 602\n", + " if PMOT < AHSTall, !- Program Line 603\n", + " set AHST = 19+AHSTtol, !- Program Line 604\n", + " elseif PMOT > AHSTaul, !- Program Line 605\n", + " set AHST = 22+AHSTtol, !- Program Line 606\n", + " endif, !- Program Line 607\n", + " elseif CAT==85, !- Program Line 608\n", + " if PMOT < AHSTall, !- Program Line 609\n", + " set AHST = 20.77+AHSTtol, !- Program Line 610\n", + " elseif PMOT > AHSTaul, !- Program Line 611\n", + " set AHST = 24.26+AHSTtol, !- Program Line 612\n", + " endif, !- Program Line 613\n", + " elseif CAT==90, !- Program Line 614\n", + " if PMOT < AHSTall, !- Program Line 615\n", + " set AHST = 20+AHSTtol, !- Program Line 616\n", + " elseif PMOT > AHSTaul, !- Program Line 617\n", + " set AHST = 23+AHSTtol, !- Program Line 618\n", + " endif, !- Program Line 619\n", + " endif, !- Program Line 620\n", + " endif, !- Program Line 621\n", + " if (ComfStand == 7) && (ComfMod == 3), !- Program Line 622\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 623\n", + " set ACST = PMOT*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 624\n", + " elseif PMOT < ACSTall, !- Program Line 625\n", + " set ACST = ACSTall*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 626\n", + " elseif PMOT > ACSTaul, !- Program Line 627\n", + " set ACST = ACSTaul*0.54+12.83+ACSToffset+ACSTtol, !- Program Line 628\n", + " endif, !- Program Line 629\n", + " endif, !- Program Line 630\n", + " if (ComfStand == 7) && (ComfMod == 3), !- Program Line 631\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 632\n", + " set AHST = PMOT*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 633\n", + " elseif PMOT < AHSTall, !- Program Line 634\n", + " set AHST = AHSTall*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 635\n", + " elseif PMOT > AHSTaul, !- Program Line 636\n", + " set AHST = AHSTaul*0.54+12.83+AHSToffset+AHSTtol, !- Program Line 637\n", + " endif, !- Program Line 638\n", + " endif, !- Program Line 639\n", + " if (ComfStand == 8) && (ComfMod == 1), !- Program Line 640\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 641\n", + " set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 642\n", + " else, !- Program Line 643\n", + " if CAT==80, !- Program Line 644\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 645\n", + " elseif CAT==85, !- Program Line 646\n", + " set ACST = PMOT*0.078+23.25+2.11+ACSTtol, !- Program Line 647\n", + " elseif CAT==90, !- Program Line 648\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 649\n", + " endif, !- Program Line 650\n", + " endif, !- Program Line 651\n", + " endif, !- Program Line 652\n", + " if (ComfStand == 8) && (ComfMod == 1), !- Program Line 653\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 654\n", + " set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 655\n", + " else, !- Program Line 656\n", + " if CAT==80, !- Program Line 657\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 658\n", + " elseif CAT==85, !- Program Line 659\n", + " set AHST = PMOT*0.078+23.25-2.11+AHSTtol, !- Program Line 660\n", + " elseif CAT==90, !- Program Line 661\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 662\n", + " endif, !- Program Line 663\n", + " endif, !- Program Line 664\n", + " endif, !- Program Line 665\n", + " if (ComfStand == 8) && (ComfMod == 2), !- Program Line 666\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 667\n", + " set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 668\n", + " elseif CAT==80, !- Program Line 669\n", + " if PMOT < ACSTall, !- Program Line 670\n", + " set ACST = 25+ACSTtol, !- Program Line 671\n", + " elseif PMOT > ACSTaul, !- Program Line 672\n", + " set ACST = 27+ACSTtol, !- Program Line 673\n", + " endif, !- Program Line 674\n", + " elseif CAT==85, !- Program Line 675\n", + " if PMOT < ACSTall, !- Program Line 676\n", + " set ACST = 25.72+ACSTtol, !- Program Line 677\n", + " elseif PMOT > ACSTaul, !- Program Line 678\n", + " set ACST = 27.89+ACSTtol, !- Program Line 679\n", + " endif, !- Program Line 680\n", + " elseif CAT==90, !- Program Line 681\n", + " if PMOT < ACSTall, !- Program Line 682\n", + " set ACST = 24+ACSTtol, !- Program Line 683\n", + " elseif PMOT > ACSTaul, !- Program Line 684\n", + " set ACST = 26+ACSTtol, !- Program Line 685\n", + " endif, !- Program Line 686\n", + " endif, !- Program Line 687\n", + " endif, !- Program Line 688\n", + " if (ComfStand == 8) && (ComfMod == 2), !- Program Line 689\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 690\n", + " set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 691\n", + " elseif CAT==80, !- Program Line 692\n", + " if PMOT < AHSTall, !- Program Line 693\n", + " set AHST = 19+AHSTtol, !- Program Line 694\n", + " elseif PMOT > AHSTaul, !- Program Line 695\n", + " set AHST = 22+AHSTtol, !- Program Line 696\n", + " endif, !- Program Line 697\n", + " elseif CAT==85, !- Program Line 698\n", + " if PMOT < AHSTall, !- Program Line 699\n", + " set AHST = 20.77+AHSTtol, !- Program Line 700\n", + " elseif PMOT > AHSTaul, !- Program Line 701\n", + " set AHST = 24.26+AHSTtol, !- Program Line 702\n", + " endif, !- Program Line 703\n", + " elseif CAT==90, !- Program Line 704\n", + " if PMOT < AHSTall, !- Program Line 705\n", + " set AHST = 20+AHSTtol, !- Program Line 706\n", + " elseif PMOT > AHSTaul, !- Program Line 707\n", + " set AHST = 23+AHSTtol, !- Program Line 708\n", + " endif, !- Program Line 709\n", + " endif, !- Program Line 710\n", + " endif, !- Program Line 711\n", + " if (ComfStand == 8) && (ComfMod == 3), !- Program Line 712\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 713\n", + " set ACST = PMOT*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 714\n", + " elseif PMOT < ACSTall, !- Program Line 715\n", + " set ACST = ACSTall*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 716\n", + " elseif PMOT > ACSTaul, !- Program Line 717\n", + " set ACST = ACSTaul*0.28+17.87+ACSToffset+ACSTtol, !- Program Line 718\n", + " endif, !- Program Line 719\n", + " endif, !- Program Line 720\n", + " if (ComfStand == 8) && (ComfMod == 3), !- Program Line 721\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 722\n", + " set AHST = PMOT*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 723\n", + " elseif PMOT < AHSTall, !- Program Line 724\n", + " set AHST = AHSTall*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 725\n", + " elseif PMOT > AHSTaul, !- Program Line 726\n", + " set AHST = AHSTaul*0.28+17.87+AHSToffset+AHSTtol, !- Program Line 727\n", + " endif, !- Program Line 728\n", + " endif, !- Program Line 729\n", + " if (ComfStand == 9) && (ComfMod == 1), !- Program Line 730\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 731\n", + " set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 732\n", + " else, !- Program Line 733\n", + " if CAT==80, !- Program Line 734\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 735\n", + " elseif CAT==90, !- Program Line 736\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 737\n", + " endif, !- Program Line 738\n", + " endif, !- Program Line 739\n", + " endif, !- Program Line 740\n", + " if (ComfStand == 9) && (ComfMod == 1), !- Program Line 741\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 742\n", + " set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 743\n", + " else, !- Program Line 744\n", + " if CAT==80, !- Program Line 745\n", + " set AHST = PMOT*0.078+23.25+2.72+AHSTtol, !- Program Line 746\n", + " elseif CAT==90, !- Program Line 747\n", + " set AHST = PMOT*0.078+23.25+1.5+AHSTtol, !- Program Line 748\n", + " endif, !- Program Line 749\n", + " endif, !- Program Line 750\n", + " endif, !- Program Line 751\n", + " if (ComfStand == 9) && (ComfMod == 2), !- Program Line 752\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 753\n", + " set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 754\n", + " elseif CAT==80, !- Program Line 755\n", + " if PMOT < ACSTall, !- Program Line 756\n", + " set ACST = 25+ACSTtol, !- Program Line 757\n", + " elseif PMOT > ACSTaul, !- Program Line 758\n", + " set ACST = 27+ACSTtol, !- Program Line 759\n", + " endif, !- Program Line 760\n", + " elseif CAT==90, !- Program Line 761\n", + " if PMOT < ACSTall, !- Program Line 762\n", + " set ACST = 24+ACSTtol, !- Program Line 763\n", + " elseif PMOT > ACSTaul, !- Program Line 764\n", + " set ACST = 26+ACSTtol, !- Program Line 765\n", + " endif, !- Program Line 766\n", + " endif, !- Program Line 767\n", + " endif, !- Program Line 768\n", + " if (ComfStand == 9) && (ComfMod == 2), !- Program Line 769\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 770\n", + " set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 771\n", + " elseif CAT==80, !- Program Line 772\n", + " if PMOT < AHSTall, !- Program Line 773\n", + " set AHST = 19+AHSTtol, !- Program Line 774\n", + " elseif PMOT > AHSTaul, !- Program Line 775\n", + " set AHST = 22+AHSTtol, !- Program Line 776\n", + " endif, !- Program Line 777\n", + " elseif CAT==90, !- Program Line 778\n", + " if PMOT < AHSTall, !- Program Line 779\n", + " set AHST = 20+AHSTtol, !- Program Line 780\n", + " elseif PMOT > AHSTaul, !- Program Line 781\n", + " set AHST = 23+AHSTtol, !- Program Line 782\n", + " endif, !- Program Line 783\n", + " endif, !- Program Line 784\n", + " endif, !- Program Line 785\n", + " if (ComfStand == 9) && (ComfMod == 3), !- Program Line 786\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 787\n", + " set ACST = PMOT*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 788\n", + " elseif PMOT < ACSTall, !- Program Line 789\n", + " set ACST = ACSTall*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 790\n", + " elseif PMOT > ACSTaul, !- Program Line 791\n", + " set ACST = ACSTaul*0.39+18.42+ACSToffset+ACSTtol, !- Program Line 792\n", + " endif, !- Program Line 793\n", + " endif, !- Program Line 794\n", + " if (ComfStand == 9) && (ComfMod == 3), !- Program Line 795\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 796\n", + " set AHST = PMOT*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 797\n", + " elseif PMOT < AHSTall, !- Program Line 798\n", + " set AHST = AHSTall*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 799\n", + " elseif PMOT > AHSTaul, !- Program Line 800\n", + " set AHST = AHSTaul*0.39+18.42+AHSToffset+AHSTtol, !- Program Line 801\n", + " endif, !- Program Line 802\n", + " endif, !- Program Line 803\n", + " if (ComfStand == 10) && (ComfMod == 1), !- Program Line 804\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 805\n", + " set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 806\n", + " else, !- Program Line 807\n", + " if CAT==80, !- Program Line 808\n", + " set ACST = RMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 809\n", + " elseif CAT==90, !- Program Line 810\n", + " set ACST = RMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 811\n", + " endif, !- Program Line 812\n", + " endif, !- Program Line 813\n", + " endif, !- Program Line 814\n", + " if (ComfStand == 10) && (ComfMod == 1), !- Program Line 815\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 816\n", + " set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 817\n", + " else, !- Program Line 818\n", + " if CAT==80, !- Program Line 819\n", + " set AHST = RMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 820\n", + " elseif CAT==90, !- Program Line 821\n", + " set AHST = RMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 822\n", + " endif, !- Program Line 823\n", + " endif, !- Program Line 824\n", + " endif, !- Program Line 825\n", + " if (ComfStand == 10) && (ComfMod == 2), !- Program Line 826\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 827\n", + " set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 828\n", + " elseif CAT==80, !- Program Line 829\n", + " if RMOT < ACSTall, !- Program Line 830\n", + " set ACST = 25+ACSTtol, !- Program Line 831\n", + " elseif RMOT > ACSTaul, !- Program Line 832\n", + " set ACST = 27+ACSTtol, !- Program Line 833\n", + " endif, !- Program Line 834\n", + " elseif CAT==90, !- Program Line 835\n", + " if RMOT < ACSTall, !- Program Line 836\n", + " set ACST = 24+ACSTtol, !- Program Line 837\n", + " elseif RMOT > ACSTaul, !- Program Line 838\n", + " set ACST = 26+ACSTtol, !- Program Line 839\n", + " endif, !- Program Line 840\n", + " endif, !- Program Line 841\n", + " endif, !- Program Line 842\n", + " if (ComfStand == 10) && (ComfMod == 2), !- Program Line 843\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 844\n", + " set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 845\n", + " elseif CAT==80, !- Program Line 846\n", + " if RMOT < AHSTall, !- Program Line 847\n", + " set AHST = 19+AHSTtol, !- Program Line 848\n", + " elseif RMOT > AHSTaul, !- Program Line 849\n", + " set AHST = 22+AHSTtol, !- Program Line 850\n", + " endif, !- Program Line 851\n", + " elseif CAT==90, !- Program Line 852\n", + " if RMOT < AHSTall, !- Program Line 853\n", + " set AHST = 20+AHSTtol, !- Program Line 854\n", + " elseif RMOT > AHSTaul, !- Program Line 855\n", + " set AHST = 23+AHSTtol, !- Program Line 856\n", + " endif, !- Program Line 857\n", + " endif, !- Program Line 858\n", + " endif, !- Program Line 859\n", + " if (ComfStand == 10) && (ComfMod == 3), !- Program Line 860\n", + " if (RMOT >= ACSTall) && (RMOT <= ACSTaul), !- Program Line 861\n", + " set ACST = RMOT*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 862\n", + " elseif RMOT < ACSTall, !- Program Line 863\n", + " set ACST = ACSTall*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 864\n", + " elseif RMOT > ACSTaul, !- Program Line 865\n", + " set ACST = ACSTaul*0.42+17.6+ACSToffset+ACSTtol, !- Program Line 866\n", + " endif, !- Program Line 867\n", + " endif, !- Program Line 868\n", + " if (ComfStand == 10) && (ComfMod == 3), !- Program Line 869\n", + " if (RMOT >= AHSTall) && (RMOT <= AHSTaul), !- Program Line 870\n", + " set AHST = RMOT*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 871\n", + " elseif RMOT < AHSTall, !- Program Line 872\n", + " set AHST = AHSTall*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 873\n", + " elseif RMOT > AHSTaul, !- Program Line 874\n", + " set AHST = AHSTaul*0.42+17.6+AHSToffset+AHSTtol, !- Program Line 875\n", + " endif, !- Program Line 876\n", + " endif, !- Program Line 877\n", + " if (ComfStand == 11) && (ComfMod == 1), !- Program Line 878\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 879\n", + " set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 880\n", + " else, !- Program Line 881\n", + " if CAT==80, !- Program Line 882\n", + " set ACST = PMOT*0.078+23.25+2.72+ACSTtol, !- Program Line 883\n", + " elseif CAT==90, !- Program Line 884\n", + " set ACST = PMOT*0.078+23.25+1.5+ACSTtol, !- Program Line 885\n", + " endif, !- Program Line 886\n", + " endif, !- Program Line 887\n", + " endif, !- Program Line 888\n", + " if (ComfStand == 11) && (ComfMod == 1), !- Program Line 889\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 890\n", + " set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 891\n", + " else, !- Program Line 892\n", + " if CAT==80, !- Program Line 893\n", + " set AHST = PMOT*0.078+23.25-2.72+AHSTtol, !- Program Line 894\n", + " elseif CAT==90, !- Program Line 895\n", + " set AHST = PMOT*0.078+23.25-1.5+AHSTtol, !- Program Line 896\n", + " endif, !- Program Line 897\n", + " endif, !- Program Line 898\n", + " endif, !- Program Line 899\n", + " if (ComfStand == 11) && (ComfMod == 2), !- Program Line 900\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 901\n", + " set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 902\n", + " elseif CAT==80, !- Program Line 903\n", + " if PMOT < ACSTall, !- Program Line 904\n", + " set ACST = 25+ACSTtol, !- Program Line 905\n", + " elseif PMOT > ACSTaul, !- Program Line 906\n", + " set ACST = 27+ACSTtol, !- Program Line 907\n", + " endif, !- Program Line 908\n", + " elseif CAT==90, !- Program Line 909\n", + " if PMOT < ACSTall, !- Program Line 910\n", + " set ACST = 24+ACSTtol, !- Program Line 911\n", + " elseif PMOT > ACSTaul, !- Program Line 912\n", + " set ACST = 26+ACSTtol, !- Program Line 913\n", + " endif, !- Program Line 914\n", + " endif, !- Program Line 915\n", + " endif, !- Program Line 916\n", + " if (ComfStand == 11) && (ComfMod == 2), !- Program Line 917\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 918\n", + " set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 919\n", + " elseif CAT==80, !- Program Line 920\n", + " if PMOT < AHSTall, !- Program Line 921\n", + " set AHST = 19+AHSTtol, !- Program Line 922\n", + " elseif PMOT > AHSTaul, !- Program Line 923\n", + " set AHST = 22+AHSTtol, !- Program Line 924\n", + " endif, !- Program Line 925\n", + " elseif CAT==90, !- Program Line 926\n", + " if PMOT < AHSTall, !- Program Line 927\n", + " set AHST = 20+AHSTtol, !- Program Line 928\n", + " elseif PMOT > AHSTaul, !- Program Line 929\n", + " set AHST = 23+AHSTtol, !- Program Line 930\n", + " endif, !- Program Line 931\n", + " endif, !- Program Line 932\n", + " endif, !- Program Line 933\n", + " if (ComfStand == 11) && (ComfMod == 3), !- Program Line 934\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 935\n", + " set ACST = PMOT*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 936\n", + " elseif PMOT < ACSTall, !- Program Line 937\n", + " set ACST = ACSTall*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 938\n", + " elseif PMOT > ACSTaul, !- Program Line 939\n", + " set ACST = ACSTaul*0.75+5.37+ACSToffset+ACSTtol, !- Program Line 940\n", + " endif, !- Program Line 941\n", + " endif, !- Program Line 942\n", + " if (ComfStand == 11) && (ComfMod == 3), !- Program Line 943\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 944\n", + " set AHST = PMOT*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 945\n", + " elseif PMOT < AHSTall, !- Program Line 946\n", + " set AHST = AHSTall*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 947\n", + " elseif PMOT > AHSTaul, !- Program Line 948\n", + " set AHST = AHSTaul*0.75+5.37+AHSToffset+AHSTtol, !- Program Line 949\n", + " endif, !- Program Line 950\n", + " endif, !- Program Line 951\n", + " if (ComfStand == 12) && (ComfMod == 0), !- Program Line 952\n", + " if (CAT==80), !- Program Line 953\n", + " set ACST = 27+ACSTtol, !- Program Line 954\n", + " elseif (CAT==90), !- Program Line 955\n", + " set ACST = 25.5+ACSTtol, !- Program Line 956\n", + " endif, !- Program Line 957\n", + " endif, !- Program Line 958\n", + " if (ComfStand == 12) && (ComfMod == 0), !- Program Line 959\n", + " if (CAT==80), !- Program Line 960\n", + " set AHST = 20+AHSTtol, !- Program Line 961\n", + " elseif (CAT==90), !- Program Line 962\n", + " set AHST = 21.5+AHSTtol, !- Program Line 963\n", + " endif, !- Program Line 964\n", + " endif, !- Program Line 965\n", + " if (ComfStand == 12) && (ComfMod == 1), !- Program Line 966\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 967\n", + " set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 968\n", + " elseif CAT==80, !- Program Line 969\n", + " if PMOT < ACSTall, !- Program Line 970\n", + " set ACST = 27+ACSTtol, !- Program Line 971\n", + " elseif PMOT > ACSTaul, !- Program Line 972\n", + " set ACST = 27+ACSTtol, !- Program Line 973\n", + " endif, !- Program Line 974\n", + " elseif CAT==90, !- Program Line 975\n", + " if PMOT < ACSTall, !- Program Line 976\n", + " set ACST = 25.5+ACSTtol, !- Program Line 977\n", + " elseif PMOT > ACSTaul, !- Program Line 978\n", + " set ACST = 25.5+ACSTtol, !- Program Line 979\n", + " endif, !- Program Line 980\n", + " endif, !- Program Line 981\n", + " endif, !- Program Line 982\n", + " if (ComfStand == 12) && (ComfMod == 1), !- Program Line 983\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 984\n", + " set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 985\n", + " elseif CAT==80, !- Program Line 986\n", + " if PMOT < AHSTall, !- Program Line 987\n", + " set AHST = 20+AHSTtol, !- Program Line 988\n", + " elseif PMOT > AHSTaul, !- Program Line 989\n", + " set AHST = 20+AHSTtol, !- Program Line 990\n", + " endif, !- Program Line 991\n", + " elseif CAT==90, !- Program Line 992\n", + " if PMOT < AHSTall, !- Program Line 993\n", + " set AHST = 21.5+AHSTtol, !- Program Line 994\n", + " elseif PMOT > AHSTaul, !- Program Line 995\n", + " set AHST = 21.5+AHSTtol, !- Program Line 996\n", + " endif, !- Program Line 997\n", + " endif, !- Program Line 998\n", + " endif, !- Program Line 999\n", + " if (ComfStand == 12) && (ComfMod == 2), !- Program Line 1000\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1001\n", + " set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1002\n", + " elseif CAT==80, !- Program Line 1003\n", + " if PMOT < ACSTall, !- Program Line 1004\n", + " set ACST = 25+ACSTtol, !- Program Line 1005\n", + " elseif PMOT > ACSTaul, !- Program Line 1006\n", + " set ACST = 27+ACSTtol, !- Program Line 1007\n", + " endif, !- Program Line 1008\n", + " elseif CAT==90, !- Program Line 1009\n", + " if PMOT < ACSTall, !- Program Line 1010\n", + " set ACST = 24+ACSTtol, !- Program Line 1011\n", + " elseif PMOT > ACSTaul, !- Program Line 1012\n", + " set ACST = 26+ACSTtol, !- Program Line 1013\n", + " endif, !- Program Line 1014\n", + " endif, !- Program Line 1015\n", + " endif, !- Program Line 1016\n", + " if (ComfStand == 12) && (ComfMod == 2), !- Program Line 1017\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1018\n", + " set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1019\n", + " elseif CAT==80, !- Program Line 1020\n", + " if PMOT < AHSTall, !- Program Line 1021\n", + " set AHST = 19+AHSTtol, !- Program Line 1022\n", + " elseif PMOT > AHSTaul, !- Program Line 1023\n", + " set AHST = 22+AHSTtol, !- Program Line 1024\n", + " endif, !- Program Line 1025\n", + " elseif CAT==90, !- Program Line 1026\n", + " if PMOT < AHSTall, !- Program Line 1027\n", + " set AHST = 20+AHSTtol, !- Program Line 1028\n", + " elseif PMOT > AHSTaul, !- Program Line 1029\n", + " set AHST = 23+AHSTtol, !- Program Line 1030\n", + " endif, !- Program Line 1031\n", + " endif, !- Program Line 1032\n", + " endif, !- Program Line 1033\n", + " if (ComfStand == 12) && (ComfMod == 3), !- Program Line 1034\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1035\n", + " set ACST = PMOT*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1036\n", + " elseif PMOT < ACSTall, !- Program Line 1037\n", + " set ACST = ACSTall*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1038\n", + " elseif PMOT > ACSTaul, !- Program Line 1039\n", + " set ACST = ACSTaul*0.25+19.7+ACSToffset+ACSTtol, !- Program Line 1040\n", + " endif, !- Program Line 1041\n", + " endif, !- Program Line 1042\n", + " if (ComfStand == 12) && (ComfMod == 3), !- Program Line 1043\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1044\n", + " set AHST = PMOT*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1045\n", + " elseif PMOT < AHSTall, !- Program Line 1046\n", + " set AHST = AHSTall*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1047\n", + " elseif PMOT > AHSTaul, !- Program Line 1048\n", + " set AHST = AHSTaul*0.25+19.7+AHSToffset+AHSTtol, !- Program Line 1049\n", + " endif, !- Program Line 1050\n", + " endif, !- Program Line 1051\n", + " if (ComfStand == 13) || (ComfStand == 14), !- Program Line 1052\n", + " if (CAT==80), !- Program Line 1053\n", + " if (CurrentTime > 6) && (CurrentTime < 23), !- Program Line 1054\n", + " if (ComfMod == 0.1), !- Program Line 1055\n", + " set ACST = 27+ACSTtol, !- Program Line 1056\n", + " elseif (ComfMod == 0.2), !- Program Line 1057\n", + " set ACST = 26+ACSTtol, !- Program Line 1058\n", + " elseif (ComfMod == 0.3), !- Program Line 1059\n", + " set ACST = 25+ACSTtol, !- Program Line 1060\n", + " elseif (ComfMod == 0.4), !- Program Line 1061\n", + " set ACST = 24+ACSTtol, !- Program Line 1062\n", + " elseif (ComfMod == 0.5), !- Program Line 1063\n", + " set ACST = 23+ACSTtol, !- Program Line 1064\n", + " endif, !- Program Line 1065\n", + " else, !- Program Line 1066\n", + " set ACST = 24+ACSTtol, !- Program Line 1067\n", + " endif, !- Program Line 1068\n", + " elseif (CAT==90), !- Program Line 1069\n", + " if (CurrentTime > 6) && (CurrentTime < 23), !- Program Line 1070\n", + " if (ComfMod == 0.1), !- Program Line 1071\n", + " set ACST = 26+ACSTtol, !- Program Line 1072\n", + " elseif (ComfMod == 0.2), !- Program Line 1073\n", + " set ACST = 25+ACSTtol, !- Program Line 1074\n", + " elseif (ComfMod == 0.3), !- Program Line 1075\n", + " set ACST = 24+ACSTtol, !- Program Line 1076\n", + " elseif (ComfMod == 0.4), !- Program Line 1077\n", + " set ACST = 23+ACSTtol, !- Program Line 1078\n", + " elseif (ComfMod == 0.5), !- Program Line 1079\n", + " set ACST = 22+ACSTtol, !- Program Line 1080\n", + " endif, !- Program Line 1081\n", + " else, !- Program Line 1082\n", + " set ACST = 23+ACSTtol, !- Program Line 1083\n", + " endif, !- Program Line 1084\n", + " endif, !- Program Line 1085\n", + " endif, !- Program Line 1086\n", + " if (ComfStand == 13) || (ComfStand == 14), !- Program Line 1087\n", + " if (ComfMod == 0.1) || (ComfMod == 0.2) || (ComfMod == 0.3) || (ComfMod == 0.4) || (ComfMod == 0.5), !- Program Line 1088\n", + " if (CAT==80), !- Program Line 1089\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1090\n", + " set AHST = 20+AHSTtol, !- Program Line 1091\n", + " else, !- Program Line 1092\n", + " set AHST = 18+AHSTtol, !- Program Line 1093\n", + " endif, !- Program Line 1094\n", + " elseif (CAT==90), !- Program Line 1095\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1096\n", + " set AHST = 21+AHSTtol, !- Program Line 1097\n", + " else, !- Program Line 1098\n", + " set AHST = 19+AHSTtol, !- Program Line 1099\n", + " endif, !- Program Line 1100\n", + " endif, !- Program Line 1101\n", + " endif, !- Program Line 1102\n", + " endif, !- Program Line 1103\n", + " if (ComfStand == 13), !- Program Line 1104\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1105\n", + " if CAT == 80, !- Program Line 1106\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1107\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1108\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1109\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1110\n", + " set ACST = 27+ACSTtol, !- Program Line 1111\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1112\n", + " set ACST = 26+ACSTtol, !- Program Line 1113\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1114\n", + " set ACST = 25+ACSTtol, !- Program Line 1115\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1116\n", + " set ACST = 24+ACSTtol, !- Program Line 1117\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1118\n", + " set ACST = 23+ACSTtol, !- Program Line 1119\n", + " else, !- Program Line 1120\n", + " set ACST = 24+ACSTtol, !- Program Line 1121\n", + " endif, !- Program Line 1122\n", + " endif, !- Program Line 1123\n", + " elseif CAT==90, !- Program Line 1124\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1125\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1126\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1127\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1128\n", + " set ACST = 26+ACSTtol, !- Program Line 1129\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1130\n", + " set ACST = 25+ACSTtol, !- Program Line 1131\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1132\n", + " set ACST = 24+ACSTtol, !- Program Line 1133\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1134\n", + " set ACST = 23+ACSTtol, !- Program Line 1135\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1136\n", + " set ACST = 22+ACSTtol, !- Program Line 1137\n", + " else, !- Program Line 1138\n", + " set ACST = 23+ACSTtol, !- Program Line 1139\n", + " endif, !- Program Line 1140\n", + " endif, !- Program Line 1141\n", + " endif, !- Program Line 1142\n", + " endif, !- Program Line 1143\n", + " endif, !- Program Line 1144\n", + " if (ComfStand == 13), !- Program Line 1145\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1146\n", + " if CAT == 80, !- Program Line 1147\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1148\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1149\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1150\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1151\n", + " set AHST = 20+AHSTtol, !- Program Line 1152\n", + " else, !- Program Line 1153\n", + " set AHST = 18+AHSTtol, !- Program Line 1154\n", + " endif, !- Program Line 1155\n", + " endif, !- Program Line 1156\n", + " elseif CAT==90, !- Program Line 1157\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1158\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1159\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1160\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1161\n", + " set AHST = 21+AHSTtol, !- Program Line 1162\n", + " else, !- Program Line 1163\n", + " set AHST = 19+AHSTtol, !- Program Line 1164\n", + " endif, !- Program Line 1165\n", + " endif, !- Program Line 1166\n", + " endif, !- Program Line 1167\n", + " endif, !- Program Line 1168\n", + " endif, !- Program Line 1169\n", + " if (ComfStand == 13) && (ComfMod == 2), !- Program Line 1170\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1171\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1172\n", + " elseif CAT==80, !- Program Line 1173\n", + " if PMOT < ACSTall, !- Program Line 1174\n", + " set ACST = 25+ACSTtol, !- Program Line 1175\n", + " elseif PMOT > ACSTaul, !- Program Line 1176\n", + " set ACST = 27+ACSTtol, !- Program Line 1177\n", + " endif, !- Program Line 1178\n", + " elseif CAT==90, !- Program Line 1179\n", + " if PMOT < ACSTall, !- Program Line 1180\n", + " set ACST = 24+ACSTtol, !- Program Line 1181\n", + " elseif PMOT > ACSTaul, !- Program Line 1182\n", + " set ACST = 26+ACSTtol, !- Program Line 1183\n", + " endif, !- Program Line 1184\n", + " endif, !- Program Line 1185\n", + " endif, !- Program Line 1186\n", + " if (ComfStand == 13) && (ComfMod == 2), !- Program Line 1187\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1188\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1189\n", + " elseif CAT==80, !- Program Line 1190\n", + " if PMOT < AHSTall, !- Program Line 1191\n", + " set AHST = 19+AHSTtol, !- Program Line 1192\n", + " elseif PMOT > AHSTaul, !- Program Line 1193\n", + " set AHST = 22+AHSTtol, !- Program Line 1194\n", + " endif, !- Program Line 1195\n", + " elseif CAT==90, !- Program Line 1196\n", + " if PMOT < AHSTall, !- Program Line 1197\n", + " set AHST = 20+AHSTtol, !- Program Line 1198\n", + " elseif PMOT > AHSTaul, !- Program Line 1199\n", + " set AHST = 23+AHSTtol, !- Program Line 1200\n", + " endif, !- Program Line 1201\n", + " endif, !- Program Line 1202\n", + " endif, !- Program Line 1203\n", + " if (ComfStand == 13) && (ComfMod == 3), !- Program Line 1204\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1205\n", + " set ACST = PMOT*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1206\n", + " elseif PMOT < ACSTall, !- Program Line 1207\n", + " set ACST = ACSTall*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1208\n", + " elseif PMOT > ACSTaul, !- Program Line 1209\n", + " set ACST = ACSTaul*0.26+15.9+ACSToffset+ACSTtol, !- Program Line 1210\n", + " endif, !- Program Line 1211\n", + " endif, !- Program Line 1212\n", + " if (ComfStand == 13) && (ComfMod == 3), !- Program Line 1213\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1214\n", + " set AHST = PMOT*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1215\n", + " elseif PMOT < AHSTall, !- Program Line 1216\n", + " set AHST = AHSTall*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1217\n", + " elseif PMOT > AHSTaul, !- Program Line 1218\n", + " set AHST = AHSTaul*0.26+15.9+AHSToffset+AHSTtol, !- Program Line 1219\n", + " endif, !- Program Line 1220\n", + " endif, !- Program Line 1221\n", + " if (ComfStand == 14), !- Program Line 1222\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1223\n", + " if CAT == 80, !- Program Line 1224\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1225\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1226\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1227\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1228\n", + " set ACST = 27+ACSTtol, !- Program Line 1229\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1230\n", + " set ACST = 26+ACSTtol, !- Program Line 1231\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1232\n", + " set ACST = 25+ACSTtol, !- Program Line 1233\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1234\n", + " set ACST = 24+ACSTtol, !- Program Line 1235\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1236\n", + " set ACST = 23+ACSTtol, !- Program Line 1237\n", + " else, !- Program Line 1238\n", + " set ACST = 24+ACSTtol, !- Program Line 1239\n", + " endif, !- Program Line 1240\n", + " endif, !- Program Line 1241\n", + " elseif CAT==90, !- Program Line 1242\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1243\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1244\n", + " elseif (PMOT < ACSTall) || (PMOT > ACSTaul), !- Program Line 1245\n", + " if (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.1), !- Program Line 1246\n", + " set ACST = 26+ACSTtol, !- Program Line 1247\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.2), !- Program Line 1248\n", + " set ACST = 25+ACSTtol, !- Program Line 1249\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.3), !- Program Line 1250\n", + " set ACST = 24+ACSTtol, !- Program Line 1251\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.4), !- Program Line 1252\n", + " set ACST = 23+ACSTtol, !- Program Line 1253\n", + " elseif (CurrentTime > 6) && (CurrentTime < 23) && (ComfMod == 1.5), !- Program Line 1254\n", + " set ACST = 22+ACSTtol, !- Program Line 1255\n", + " else, !- Program Line 1256\n", + " set ACST = 23+ACSTtol, !- Program Line 1257\n", + " endif, !- Program Line 1258\n", + " endif, !- Program Line 1259\n", + " endif, !- Program Line 1260\n", + " endif, !- Program Line 1261\n", + " endif, !- Program Line 1262\n", + " if (ComfStand == 14), !- Program Line 1263\n", + " if (ComfMod == 1.1) || (ComfMod == 1.2) || (ComfMod == 1.3) || (ComfMod == 1.4) || (ComfMod == 1.5), !- Program Line 1264\n", + " if CAT == 80, !- Program Line 1265\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1266\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1267\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1268\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1269\n", + " set AHST = 20+AHSTtol, !- Program Line 1270\n", + " else, !- Program Line 1271\n", + " set AHST = 18+AHSTtol, !- Program Line 1272\n", + " endif, !- Program Line 1273\n", + " endif, !- Program Line 1274\n", + " elseif CAT==90, !- Program Line 1275\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1276\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1277\n", + " elseif (PMOT < AHSTall) || (PMOT > AHSTaul), !- Program Line 1278\n", + " if (CurrentTime >6) && (CurrentTime < 12), !- Program Line 1279\n", + " set AHST = 21+AHSTtol, !- Program Line 1280\n", + " else, !- Program Line 1281\n", + " set AHST = 19+AHSTtol, !- Program Line 1282\n", + " endif, !- Program Line 1283\n", + " endif, !- Program Line 1284\n", + " endif, !- Program Line 1285\n", + " endif, !- Program Line 1286\n", + " endif, !- Program Line 1287\n", + " if (ComfStand == 14) && (ComfMod == 2), !- Program Line 1288\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1289\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1290\n", + " elseif CAT==80, !- Program Line 1291\n", + " if PMOT < ACSTall, !- Program Line 1292\n", + " set ACST = 25+ACSTtol, !- Program Line 1293\n", + " elseif PMOT > ACSTaul, !- Program Line 1294\n", + " set ACST = 27+ACSTtol, !- Program Line 1295\n", + " endif, !- Program Line 1296\n", + " elseif CAT==90, !- Program Line 1297\n", + " if PMOT < ACSTall, !- Program Line 1298\n", + " set ACST = 24+ACSTtol, !- Program Line 1299\n", + " elseif PMOT > ACSTaul, !- Program Line 1300\n", + " set ACST = 26+ACSTtol, !- Program Line 1301\n", + " endif, !- Program Line 1302\n", + " endif, !- Program Line 1303\n", + " endif, !- Program Line 1304\n", + " if (ComfStand == 14) && (ComfMod == 2), !- Program Line 1305\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1306\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1307\n", + " elseif CAT==80, !- Program Line 1308\n", + " if PMOT < AHSTall, !- Program Line 1309\n", + " set AHST = 19+AHSTtol, !- Program Line 1310\n", + " elseif PMOT > AHSTaul, !- Program Line 1311\n", + " set AHST = 22+AHSTtol, !- Program Line 1312\n", + " endif, !- Program Line 1313\n", + " elseif CAT==90, !- Program Line 1314\n", + " if PMOT < AHSTall, !- Program Line 1315\n", + " set AHST = 20+AHSTtol, !- Program Line 1316\n", + " elseif PMOT > AHSTaul, !- Program Line 1317\n", + " set AHST = 23+AHSTtol, !- Program Line 1318\n", + " endif, !- Program Line 1319\n", + " endif, !- Program Line 1320\n", + " endif, !- Program Line 1321\n", + " if (ComfStand == 14) && (ComfMod == 3), !- Program Line 1322\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1323\n", + " set ACST = PMOT*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1324\n", + " elseif PMOT < ACSTall, !- Program Line 1325\n", + " set ACST = ACSTall*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1326\n", + " elseif PMOT > ACSTaul, !- Program Line 1327\n", + " set ACST = ACSTaul*0.26+16.75+ACSToffset+ACSTtol, !- Program Line 1328\n", + " endif, !- Program Line 1329\n", + " endif, !- Program Line 1330\n", + " if (ComfStand == 14) && (ComfMod == 3), !- Program Line 1331\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1332\n", + " set AHST = PMOT*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1333\n", + " elseif PMOT < AHSTall, !- Program Line 1334\n", + " set AHST = AHSTall*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1335\n", + " elseif PMOT > AHSTaul, !- Program Line 1336\n", + " set AHST = AHSTaul*0.26+16.75+AHSToffset+AHSTtol, !- Program Line 1337\n", + " endif, !- Program Line 1338\n", + " endif, !- Program Line 1339\n", + " if (ComfStand == 15) || (ComfStand == 16), !- Program Line 1340\n", + " if (ComfMod == 0), !- Program Line 1341\n", + " if (CAT==80), !- Program Line 1342\n", + " if PMOT < 20, !- Program Line 1343\n", + " set ACST = 23.5+ACSTtol, !- Program Line 1344\n", + " set AHST = 21+ACSTtol, !- Program Line 1345\n", + " else, !- Program Line 1346\n", + " set ACST = 25.5+ACSTtol, !- Program Line 1347\n", + " set AHST = 22.5+ACSTtol, !- Program Line 1348\n", + " endif, !- Program Line 1349\n", + " elseif (CAT==90), !- Program Line 1350\n", + " if PMOT < 20, !- Program Line 1351\n", + " set ACST = 23+ACSTtol, !- Program Line 1352\n", + " set AHST = 21.5+ACSTtol, !- Program Line 1353\n", + " else, !- Program Line 1354\n", + " set ACST = 25+ACSTtol, !- Program Line 1355\n", + " set AHST = 23+ACSTtol, !- Program Line 1356\n", + " endif, !- Program Line 1357\n", + " endif, !- Program Line 1358\n", + " endif, !- Program Line 1359\n", + " endif, !- Program Line 1360\n", + " if (ComfStand == 15) && (ComfMod == 1), !- Program Line 1361\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1362\n", + " set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1363\n", + " elseif CAT==80, !- Program Line 1364\n", + " if PMOT < ACSTall, !- Program Line 1365\n", + " set ACST = 23.5+ACSTtol, !- Program Line 1366\n", + " elseif PMOT > ACSTaul, !- Program Line 1367\n", + " set ACST = 25.5+ACSTtol, !- Program Line 1368\n", + " endif, !- Program Line 1369\n", + " elseif CAT==90, !- Program Line 1370\n", + " if PMOT < ACSTall, !- Program Line 1371\n", + " set ACST = 23+ACSTtol, !- Program Line 1372\n", + " elseif PMOT > ACSTaul, !- Program Line 1373\n", + " set ACST = 25+ACSTtol, !- Program Line 1374\n", + " endif, !- Program Line 1375\n", + " endif, !- Program Line 1376\n", + " endif, !- Program Line 1377\n", + " if (ComfStand == 15) && (ComfMod == 1), !- Program Line 1378\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1379\n", + " set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1380\n", + " elseif CAT==80, !- Program Line 1381\n", + " if PMOT < AHSTall, !- Program Line 1382\n", + " set AHST = 21+AHSTtol, !- Program Line 1383\n", + " elseif PMOT > AHSTaul, !- Program Line 1384\n", + " set AHST = 22.5+AHSTtol, !- Program Line 1385\n", + " endif, !- Program Line 1386\n", + " elseif CAT==90, !- Program Line 1387\n", + " if PMOT < AHSTall, !- Program Line 1388\n", + " set AHST = 23+AHSTtol, !- Program Line 1389\n", + " elseif PMOT > AHSTaul, !- Program Line 1390\n", + " set AHST = 23+AHSTtol, !- Program Line 1391\n", + " endif, !- Program Line 1392\n", + " endif, !- Program Line 1393\n", + " endif, !- Program Line 1394\n", + " if (ComfStand == 15) && (ComfMod == 2), !- Program Line 1395\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1396\n", + " set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1397\n", + " elseif CAT==80, !- Program Line 1398\n", + " if PMOT < ACSTall, !- Program Line 1399\n", + " set ACST = 25+ACSTtol, !- Program Line 1400\n", + " elseif PMOT > ACSTaul, !- Program Line 1401\n", + " set ACST = 27+ACSTtol, !- Program Line 1402\n", + " endif, !- Program Line 1403\n", + " elseif CAT==90, !- Program Line 1404\n", + " if PMOT < ACSTall, !- Program Line 1405\n", + " set ACST = 24+ACSTtol, !- Program Line 1406\n", + " elseif PMOT > ACSTaul, !- Program Line 1407\n", + " set ACST = 26+ACSTtol, !- Program Line 1408\n", + " endif, !- Program Line 1409\n", + " endif, !- Program Line 1410\n", + " endif, !- Program Line 1411\n", + " if (ComfStand == 15) && (ComfMod == 2), !- Program Line 1412\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1413\n", + " set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1414\n", + " elseif CAT==80, !- Program Line 1415\n", + " if PMOT < AHSTall, !- Program Line 1416\n", + " set AHST = 19+AHSTtol, !- Program Line 1417\n", + " elseif PMOT > AHSTaul, !- Program Line 1418\n", + " set AHST = 22+AHSTtol, !- Program Line 1419\n", + " endif, !- Program Line 1420\n", + " elseif CAT==90, !- Program Line 1421\n", + " if PMOT < AHSTall, !- Program Line 1422\n", + " set AHST = 20+AHSTtol, !- Program Line 1423\n", + " elseif PMOT > AHSTaul, !- Program Line 1424\n", + " set AHST = 23+AHSTtol, !- Program Line 1425\n", + " endif, !- Program Line 1426\n", + " endif, !- Program Line 1427\n", + " endif, !- Program Line 1428\n", + " if (ComfStand == 15) && (ComfMod == 3), !- Program Line 1429\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1430\n", + " set ACST = PMOT*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1431\n", + " elseif PMOT < ACSTall, !- Program Line 1432\n", + " set ACST = ACSTall*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1433\n", + " elseif PMOT > ACSTaul, !- Program Line 1434\n", + " set ACST = ACSTaul*0.56+12.74+ACSToffset+ACSTtol, !- Program Line 1435\n", + " endif, !- Program Line 1436\n", + " endif, !- Program Line 1437\n", + " if (ComfStand == 15) && (ComfMod == 3), !- Program Line 1438\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1439\n", + " set AHST = PMOT*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1440\n", + " elseif PMOT < AHSTall, !- Program Line 1441\n", + " set AHST = AHSTall*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1442\n", + " elseif PMOT > AHSTaul, !- Program Line 1443\n", + " set AHST = AHSTaul*0.56+12.74+AHSToffset+AHSTtol, !- Program Line 1444\n", + " endif, !- Program Line 1445\n", + " endif, !- Program Line 1446\n", + " if (ComfStand == 16) && (ComfMod == 1), !- Program Line 1447\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1448\n", + " set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1449\n", + " elseif CAT==80, !- Program Line 1450\n", + " if PMOT < ACSTall, !- Program Line 1451\n", + " set ACST = 23.5+ACSTtol, !- Program Line 1452\n", + " elseif PMOT > ACSTaul, !- Program Line 1453\n", + " set ACST = 25.5+ACSTtol, !- Program Line 1454\n", + " endif, !- Program Line 1455\n", + " elseif CAT==90, !- Program Line 1456\n", + " if PMOT < ACSTall, !- Program Line 1457\n", + " set ACST = 23+ACSTtol, !- Program Line 1458\n", + " elseif PMOT > ACSTaul, !- Program Line 1459\n", + " set ACST = 25+ACSTtol, !- Program Line 1460\n", + " endif, !- Program Line 1461\n", + " endif, !- Program Line 1462\n", + " endif, !- Program Line 1463\n", + " if (ComfStand == 16) && (ComfMod == 1), !- Program Line 1464\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1465\n", + " set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1466\n", + " elseif CAT==80, !- Program Line 1467\n", + " if PMOT < AHSTall, !- Program Line 1468\n", + " set AHST = 21+AHSTtol, !- Program Line 1469\n", + " elseif PMOT > AHSTaul, !- Program Line 1470\n", + " set AHST = 22.5+AHSTtol, !- Program Line 1471\n", + " endif, !- Program Line 1472\n", + " elseif CAT==90, !- Program Line 1473\n", + " if PMOT < AHSTall, !- Program Line 1474\n", + " set AHST = 23+AHSTtol, !- Program Line 1475\n", + " elseif PMOT > AHSTaul, !- Program Line 1476\n", + " set AHST = 23+AHSTtol, !- Program Line 1477\n", + " endif, !- Program Line 1478\n", + " endif, !- Program Line 1479\n", + " endif, !- Program Line 1480\n", + " if (ComfStand == 16) && (ComfMod == 2), !- Program Line 1481\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1482\n", + " set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1483\n", + " elseif CAT==80, !- Program Line 1484\n", + " if PMOT < ACSTall, !- Program Line 1485\n", + " set ACST = 25+ACSTtol, !- Program Line 1486\n", + " elseif PMOT > ACSTaul, !- Program Line 1487\n", + " set ACST = 27+ACSTtol, !- Program Line 1488\n", + " endif, !- Program Line 1489\n", + " elseif CAT==90, !- Program Line 1490\n", + " if PMOT < ACSTall, !- Program Line 1491\n", + " set ACST = 24+ACSTtol, !- Program Line 1492\n", + " elseif PMOT > ACSTaul, !- Program Line 1493\n", + " set ACST = 26+ACSTtol, !- Program Line 1494\n", + " endif, !- Program Line 1495\n", + " endif, !- Program Line 1496\n", + " endif, !- Program Line 1497\n", + " if (ComfStand == 16) && (ComfMod == 2), !- Program Line 1498\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1499\n", + " set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1500\n", + " elseif CAT==80, !- Program Line 1501\n", + " if PMOT < AHSTall, !- Program Line 1502\n", + " set AHST = 19+AHSTtol, !- Program Line 1503\n", + " elseif PMOT > AHSTaul, !- Program Line 1504\n", + " set AHST = 22+AHSTtol, !- Program Line 1505\n", + " endif, !- Program Line 1506\n", + " elseif CAT==90, !- Program Line 1507\n", + " if PMOT < AHSTall, !- Program Line 1508\n", + " set AHST = 20+AHSTtol, !- Program Line 1509\n", + " elseif PMOT > AHSTaul, !- Program Line 1510\n", + " set AHST = 23+AHSTtol, !- Program Line 1511\n", + " endif, !- Program Line 1512\n", + " endif, !- Program Line 1513\n", + " endif, !- Program Line 1514\n", + " if (ComfStand == 16) && (ComfMod == 3), !- Program Line 1515\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1516\n", + " set ACST = PMOT*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1517\n", + " elseif PMOT < ACSTall, !- Program Line 1518\n", + " set ACST = ACSTall*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1519\n", + " elseif PMOT > ACSTaul, !- Program Line 1520\n", + " set ACST = ACSTaul*0.09+22.32+ACSToffset+ACSTtol, !- Program Line 1521\n", + " endif, !- Program Line 1522\n", + " endif, !- Program Line 1523\n", + " if (ComfStand == 16) && (ComfMod == 3), !- Program Line 1524\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1525\n", + " set AHST = PMOT*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1526\n", + " elseif PMOT < AHSTall, !- Program Line 1527\n", + " set AHST = AHSTall*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1528\n", + " elseif PMOT > AHSTaul, !- Program Line 1529\n", + " set AHST = AHSTaul*0.09+22.32+AHSToffset+AHSTtol, !- Program Line 1530\n", + " endif, !- Program Line 1531\n", + " endif, !- Program Line 1532\n", + " if (ComfStand == 17) || (ComfStand == 18) || (ComfStand == 19) || (ComfStand == 20), !- Program Line 1533\n", + " if ComfMod == 0, !- Program Line 1534\n", + " set ACST = 25+ACSTtol, !- Program Line 1535\n", + " set AHST = 20+AHSTtol, !- Program Line 1536\n", + " endif, !- Program Line 1537\n", + " endif, !- Program Line 1538\n", + " if (ComfStand == 17) && (ComfMod == 1), !- Program Line 1539\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1540\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1541\n", + " set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1542\n", + " else, !- Program Line 1543\n", + " set ACST = 25+ACSTtol, !- Program Line 1544\n", + " endif, !- Program Line 1545\n", + " else, !- Program Line 1546\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1547\n", + " set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1548\n", + " else, !- Program Line 1549\n", + " set ACST = 25+ACSTtol, !- Program Line 1550\n", + " endif, !- Program Line 1551\n", + " endif, !- Program Line 1552\n", + " endif, !- Program Line 1553\n", + " if (ComfStand == 17) && (ComfMod == 1), !- Program Line 1554\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1555\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1556\n", + " set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1557\n", + " else, !- Program Line 1558\n", + " set AHST = 20+AHSTtol, !- Program Line 1559\n", + " endif, !- Program Line 1560\n", + " else, !- Program Line 1561\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1562\n", + " set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1563\n", + " else, !- Program Line 1564\n", + " set AHST = 20+AHSTtol, !- Program Line 1565\n", + " endif, !- Program Line 1566\n", + " endif, !- Program Line 1567\n", + " endif, !- Program Line 1568\n", + " if (ComfStand == 17) && (ComfMod == 2), !- Program Line 1569\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1570\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1571\n", + " set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1572\n", + " elseif PMOT < ACSTall, !- Program Line 1573\n", + " if CAT == 90, !- Program Line 1574\n", + " set ACST = 24+ACSTtol, !- Program Line 1575\n", + " elseif CAT == 80, !- Program Line 1576\n", + " set ACST = 25+ACSTtol, !- Program Line 1577\n", + " endif, !- Program Line 1578\n", + " elseif PMOT > ACSTaul, !- Program Line 1579\n", + " if CAT == 90, !- Program Line 1580\n", + " set ACST = 26+ACSTtol, !- Program Line 1581\n", + " elseif CAT == 80, !- Program Line 1582\n", + " set ACST = 27+ACSTtol, !- Program Line 1583\n", + " endif, !- Program Line 1584\n", + " endif, !- Program Line 1585\n", + " else, !- Program Line 1586\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1587\n", + " set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1588\n", + " elseif PMOT < ACSTall, !- Program Line 1589\n", + " if CAT == 90, !- Program Line 1590\n", + " set ACST = 24+ACSTtol, !- Program Line 1591\n", + " elseif CAT == 80, !- Program Line 1592\n", + " set ACST = 25+ACSTtol, !- Program Line 1593\n", + " endif, !- Program Line 1594\n", + " elseif PMOT > ACSTaul, !- Program Line 1595\n", + " if CAT == 90, !- Program Line 1596\n", + " set ACST = 26+ACSTtol, !- Program Line 1597\n", + " elseif CAT == 80, !- Program Line 1598\n", + " set ACST = 27+ACSTtol, !- Program Line 1599\n", + " endif, !- Program Line 1600\n", + " endif, !- Program Line 1601\n", + " endif, !- Program Line 1602\n", + " endif, !- Program Line 1603\n", + " if (ComfStand == 17) && (ComfMod == 2), !- Program Line 1604\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1605\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1606\n", + " set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1607\n", + " elseif PMOT < AHSTall, !- Program Line 1608\n", + " if CAT == 90, !- Program Line 1609\n", + " set AHST = 20+AHSTtol, !- Program Line 1610\n", + " elseif CAT == 80, !- Program Line 1611\n", + " set AHST = 19+AHSTtol, !- Program Line 1612\n", + " endif, !- Program Line 1613\n", + " elseif PMOT > AHSTaul, !- Program Line 1614\n", + " if CAT == 90, !- Program Line 1615\n", + " set AHST = 23+AHSTtol, !- Program Line 1616\n", + " elseif CAT == 80, !- Program Line 1617\n", + " set AHST = 22+AHSTtol, !- Program Line 1618\n", + " endif, !- Program Line 1619\n", + " endif, !- Program Line 1620\n", + " else, !- Program Line 1621\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1622\n", + " set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1623\n", + " elseif PMOT < AHSTall, !- Program Line 1624\n", + " if CAT == 90, !- Program Line 1625\n", + " set AHST = 20+AHSTtol, !- Program Line 1626\n", + " elseif CAT == 80, !- Program Line 1627\n", + " set AHST = 19+AHSTtol, !- Program Line 1628\n", + " endif, !- Program Line 1629\n", + " elseif PMOT > AHSTaul, !- Program Line 1630\n", + " if CAT == 90, !- Program Line 1631\n", + " set AHST = 23+AHSTtol, !- Program Line 1632\n", + " elseif CAT == 80, !- Program Line 1633\n", + " set AHST = 22+AHSTtol, !- Program Line 1634\n", + " endif, !- Program Line 1635\n", + " endif, !- Program Line 1636\n", + " endif, !- Program Line 1637\n", + " endif, !- Program Line 1638\n", + " if (ComfStand == 17) && (ComfMod == 3), !- Program Line 1639\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1640\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1641\n", + " set ACST = PMOT*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1642\n", + " elseif PMOT < ACSTall, !- Program Line 1643\n", + " set ACST = ACSTall*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1644\n", + " elseif PMOT > ACSTaul, !- Program Line 1645\n", + " set ACST = ACSTaul*0.48+13.9+ACSToffset+ACSTtol, !- Program Line 1646\n", + " endif, !- Program Line 1647\n", + " else, !- Program Line 1648\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1649\n", + " set ACST = PMOT*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1650\n", + " elseif PMOT < ACSTall, !- Program Line 1651\n", + " set ACST = ACSTall*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1652\n", + " elseif PMOT > ACSTaul, !- Program Line 1653\n", + " set ACST = ACSTaul*0.59+9.6+ACSToffset+ACSTtol, !- Program Line 1654\n", + " endif, !- Program Line 1655\n", + " endif, !- Program Line 1656\n", + " endif, !- Program Line 1657\n", + " if (ComfStand == 17) && (ComfMod == 3), !- Program Line 1658\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1659\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1660\n", + " set AHST = PMOT*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1661\n", + " elseif PMOT < AHSTall, !- Program Line 1662\n", + " set AHST = AHSTall*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1663\n", + " elseif PMOT > AHSTaul, !- Program Line 1664\n", + " set AHST = AHSTaul*0.48+13.9+AHSToffset+AHSTtol, !- Program Line 1665\n", + " endif, !- Program Line 1666\n", + " else, !- Program Line 1667\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1668\n", + " set AHST = PMOT*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1669\n", + " elseif PMOT < AHSTall, !- Program Line 1670\n", + " set AHST = AHSTall*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1671\n", + " elseif PMOT > AHSTaul, !- Program Line 1672\n", + " set AHST = AHSTaul*0.59+9.6+AHSToffset+AHSTtol, !- Program Line 1673\n", + " endif, !- Program Line 1674\n", + " endif, !- Program Line 1675\n", + " endif, !- Program Line 1676\n", + " if (ComfStand == 18) && (ComfMod == 1), !- Program Line 1677\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1678\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1679\n", + " set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1680\n", + " else, !- Program Line 1681\n", + " set ACST = 25+ACSTtol, !- Program Line 1682\n", + " endif, !- Program Line 1683\n", + " else, !- Program Line 1684\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1685\n", + " set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1686\n", + " else, !- Program Line 1687\n", + " set ACST = 25+ACSTtol, !- Program Line 1688\n", + " endif, !- Program Line 1689\n", + " endif, !- Program Line 1690\n", + " endif, !- Program Line 1691\n", + " if (ComfStand == 18) && (ComfMod == 1), !- Program Line 1692\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1693\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1694\n", + " set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1695\n", + " else, !- Program Line 1696\n", + " set AHST = 20+AHSTtol, !- Program Line 1697\n", + " endif, !- Program Line 1698\n", + " else, !- Program Line 1699\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1700\n", + " set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1701\n", + " else, !- Program Line 1702\n", + " set AHST = 20+AHSTtol, !- Program Line 1703\n", + " endif, !- Program Line 1704\n", + " endif, !- Program Line 1705\n", + " endif, !- Program Line 1706\n", + " if (ComfStand == 18) && (ComfMod == 2), !- Program Line 1707\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1708\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1709\n", + " set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1710\n", + " elseif PMOT < ACSTall, !- Program Line 1711\n", + " if CAT == 90, !- Program Line 1712\n", + " set ACST = 24+ACSTtol, !- Program Line 1713\n", + " elseif CAT == 80, !- Program Line 1714\n", + " set ACST = 25+ACSTtol, !- Program Line 1715\n", + " endif, !- Program Line 1716\n", + " elseif PMOT > ACSTaul, !- Program Line 1717\n", + " if CAT == 90, !- Program Line 1718\n", + " set ACST = 26+ACSTtol, !- Program Line 1719\n", + " elseif CAT == 80, !- Program Line 1720\n", + " set ACST = 27+ACSTtol, !- Program Line 1721\n", + " endif, !- Program Line 1722\n", + " endif, !- Program Line 1723\n", + " else, !- Program Line 1724\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1725\n", + " set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1726\n", + " elseif PMOT < ACSTall, !- Program Line 1727\n", + " if CAT == 90, !- Program Line 1728\n", + " set ACST = 24+ACSTtol, !- Program Line 1729\n", + " elseif CAT == 80, !- Program Line 1730\n", + " set ACST = 25+ACSTtol, !- Program Line 1731\n", + " endif, !- Program Line 1732\n", + " elseif PMOT > ACSTaul, !- Program Line 1733\n", + " if CAT == 90, !- Program Line 1734\n", + " set ACST = 26+ACSTtol, !- Program Line 1735\n", + " elseif CAT == 80, !- Program Line 1736\n", + " set ACST = 27+ACSTtol, !- Program Line 1737\n", + " endif, !- Program Line 1738\n", + " endif, !- Program Line 1739\n", + " endif, !- Program Line 1740\n", + " endif, !- Program Line 1741\n", + " if (ComfStand == 18) && (ComfMod == 2), !- Program Line 1742\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1743\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1744\n", + " set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1745\n", + " elseif PMOT < AHSTall, !- Program Line 1746\n", + " if CAT == 90, !- Program Line 1747\n", + " set AHST = 20+AHSTtol, !- Program Line 1748\n", + " elseif CAT == 80, !- Program Line 1749\n", + " set AHST = 19+AHSTtol, !- Program Line 1750\n", + " endif, !- Program Line 1751\n", + " elseif PMOT > AHSTaul, !- Program Line 1752\n", + " if CAT == 90, !- Program Line 1753\n", + " set AHST = 23+AHSTtol, !- Program Line 1754\n", + " elseif CAT == 80, !- Program Line 1755\n", + " set AHST = 22+AHSTtol, !- Program Line 1756\n", + " endif, !- Program Line 1757\n", + " endif, !- Program Line 1758\n", + " else, !- Program Line 1759\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1760\n", + " set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1761\n", + " elseif PMOT < AHSTall, !- Program Line 1762\n", + " if CAT == 90, !- Program Line 1763\n", + " set AHST = 20+AHSTtol, !- Program Line 1764\n", + " elseif CAT == 80, !- Program Line 1765\n", + " set AHST = 19+AHSTtol, !- Program Line 1766\n", + " endif, !- Program Line 1767\n", + " elseif PMOT > AHSTaul, !- Program Line 1768\n", + " if CAT == 90, !- Program Line 1769\n", + " set AHST = 23+AHSTtol, !- Program Line 1770\n", + " elseif CAT == 80, !- Program Line 1771\n", + " set AHST = 22+AHSTtol, !- Program Line 1772\n", + " endif, !- Program Line 1773\n", + " endif, !- Program Line 1774\n", + " endif, !- Program Line 1775\n", + " endif, !- Program Line 1776\n", + " if (ComfStand == 18) && (ComfMod == 3), !- Program Line 1777\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1778\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1779\n", + " set ACST = PMOT*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1780\n", + " elseif PMOT < ACSTall, !- Program Line 1781\n", + " set ACST = ACSTall*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1782\n", + " elseif PMOT > ACSTaul, !- Program Line 1783\n", + " set ACST = ACSTaul*0.84+5.3+ACSToffset+ACSTtol, !- Program Line 1784\n", + " endif, !- Program Line 1785\n", + " else, !- Program Line 1786\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1787\n", + " set ACST = PMOT*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1788\n", + " elseif PMOT < ACSTall, !- Program Line 1789\n", + " set ACST = ACSTall*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1790\n", + " elseif PMOT > ACSTaul, !- Program Line 1791\n", + " set ACST = ACSTaul*0.96-3.6+ACSToffset+ACSTtol, !- Program Line 1792\n", + " endif, !- Program Line 1793\n", + " endif, !- Program Line 1794\n", + " endif, !- Program Line 1795\n", + " if (ComfStand == 18) && (ComfMod == 3), !- Program Line 1796\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1797\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1798\n", + " set AHST = PMOT*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1799\n", + " elseif PMOT < AHSTall, !- Program Line 1800\n", + " set AHST = AHSTall*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1801\n", + " elseif PMOT > AHSTaul, !- Program Line 1802\n", + " set AHST = AHSTaul*0.84+5.3+AHSToffset+AHSTtol, !- Program Line 1803\n", + " endif, !- Program Line 1804\n", + " else, !- Program Line 1805\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1806\n", + " set AHST = PMOT*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1807\n", + " elseif PMOT < AHSTall, !- Program Line 1808\n", + " set AHST = AHSTall*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1809\n", + " elseif PMOT > AHSTaul, !- Program Line 1810\n", + " set AHST = AHSTaul*0.96-3.6+AHSToffset+AHSTtol, !- Program Line 1811\n", + " endif, !- Program Line 1812\n", + " endif, !- Program Line 1813\n", + " endif, !- Program Line 1814\n", + " if (ComfStand == 19) && (ComfMod == 1), !- Program Line 1815\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1816\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1817\n", + " set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1818\n", + " else, !- Program Line 1819\n", + " set ACST = 25+ACSTtol, !- Program Line 1820\n", + " endif, !- Program Line 1821\n", + " else, !- Program Line 1822\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1823\n", + " set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1824\n", + " else, !- Program Line 1825\n", + " set ACST = 25+ACSTtol, !- Program Line 1826\n", + " endif, !- Program Line 1827\n", + " endif, !- Program Line 1828\n", + " endif, !- Program Line 1829\n", + " if (ComfStand == 19) && (ComfMod == 1), !- Program Line 1830\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1831\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1832\n", + " set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1833\n", + " else, !- Program Line 1834\n", + " set AHST = 20+AHSTtol, !- Program Line 1835\n", + " endif, !- Program Line 1836\n", + " else, !- Program Line 1837\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1838\n", + " set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1839\n", + " else, !- Program Line 1840\n", + " set AHST = 20+AHSTtol, !- Program Line 1841\n", + " endif, !- Program Line 1842\n", + " endif, !- Program Line 1843\n", + " endif, !- Program Line 1844\n", + " if (ComfStand == 19) && (ComfMod == 2), !- Program Line 1845\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1846\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1847\n", + " set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1848\n", + " elseif PMOT < ACSTall, !- Program Line 1849\n", + " if CAT == 90, !- Program Line 1850\n", + " set ACST = 24+ACSTtol, !- Program Line 1851\n", + " elseif CAT == 80, !- Program Line 1852\n", + " set ACST = 25+ACSTtol, !- Program Line 1853\n", + " endif, !- Program Line 1854\n", + " elseif PMOT > ACSTaul, !- Program Line 1855\n", + " if CAT == 90, !- Program Line 1856\n", + " set ACST = 26+ACSTtol, !- Program Line 1857\n", + " elseif CAT == 80, !- Program Line 1858\n", + " set ACST = 27+ACSTtol, !- Program Line 1859\n", + " endif, !- Program Line 1860\n", + " endif, !- Program Line 1861\n", + " else, !- Program Line 1862\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1863\n", + " set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1864\n", + " elseif PMOT < ACSTall, !- Program Line 1865\n", + " if CAT == 90, !- Program Line 1866\n", + " set ACST = 24+ACSTtol, !- Program Line 1867\n", + " elseif CAT == 80, !- Program Line 1868\n", + " set ACST = 25+ACSTtol, !- Program Line 1869\n", + " endif, !- Program Line 1870\n", + " elseif PMOT > ACSTaul, !- Program Line 1871\n", + " if CAT == 90, !- Program Line 1872\n", + " set ACST = 26+ACSTtol, !- Program Line 1873\n", + " elseif CAT == 80, !- Program Line 1874\n", + " set ACST = 27+ACSTtol, !- Program Line 1875\n", + " endif, !- Program Line 1876\n", + " endif, !- Program Line 1877\n", + " endif, !- Program Line 1878\n", + " endif, !- Program Line 1879\n", + " if (ComfStand == 19) && (ComfMod == 2), !- Program Line 1880\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1881\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1882\n", + " set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1883\n", + " elseif PMOT < AHSTall, !- Program Line 1884\n", + " if CAT == 90, !- Program Line 1885\n", + " set AHST = 20+AHSTtol, !- Program Line 1886\n", + " elseif CAT == 80, !- Program Line 1887\n", + " set AHST = 19+AHSTtol, !- Program Line 1888\n", + " endif, !- Program Line 1889\n", + " elseif PMOT > AHSTaul, !- Program Line 1890\n", + " if CAT == 90, !- Program Line 1891\n", + " set AHST = 23+AHSTtol, !- Program Line 1892\n", + " elseif CAT == 80, !- Program Line 1893\n", + " set AHST = 22+AHSTtol, !- Program Line 1894\n", + " endif, !- Program Line 1895\n", + " endif, !- Program Line 1896\n", + " else, !- Program Line 1897\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1898\n", + " set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1899\n", + " elseif PMOT < AHSTall, !- Program Line 1900\n", + " if CAT == 90, !- Program Line 1901\n", + " set AHST = 20+AHSTtol, !- Program Line 1902\n", + " elseif CAT == 80, !- Program Line 1903\n", + " set AHST = 19+AHSTtol, !- Program Line 1904\n", + " endif, !- Program Line 1905\n", + " elseif PMOT > AHSTaul, !- Program Line 1906\n", + " if CAT == 90, !- Program Line 1907\n", + " set AHST = 23+AHSTtol, !- Program Line 1908\n", + " elseif CAT == 80, !- Program Line 1909\n", + " set AHST = 22+AHSTtol, !- Program Line 1910\n", + " endif, !- Program Line 1911\n", + " endif, !- Program Line 1912\n", + " endif, !- Program Line 1913\n", + " endif, !- Program Line 1914\n", + " if (ComfStand == 19) && (ComfMod == 3), !- Program Line 1915\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1916\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1917\n", + " set ACST = PMOT*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1918\n", + " elseif PMOT < ACSTall, !- Program Line 1919\n", + " set ACST = ACSTall*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1920\n", + " elseif PMOT > ACSTaul, !- Program Line 1921\n", + " set ACST = ACSTaul*0.27+17.9+ACSToffset+ACSTtol, !- Program Line 1922\n", + " endif, !- Program Line 1923\n", + " else, !- Program Line 1924\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1925\n", + " set ACST = PMOT*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1926\n", + " elseif PMOT < ACSTall, !- Program Line 1927\n", + " set ACST = ACSTall*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1928\n", + " elseif PMOT > ACSTaul, !- Program Line 1929\n", + " set ACST = ACSTaul*0.53+10.3+ACSToffset+ACSTtol, !- Program Line 1930\n", + " endif, !- Program Line 1931\n", + " endif, !- Program Line 1932\n", + " endif, !- Program Line 1933\n", + " if (ComfStand == 19) && (ComfMod == 3), !- Program Line 1934\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1935\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1936\n", + " set AHST = PMOT*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1937\n", + " elseif PMOT < AHSTall, !- Program Line 1938\n", + " set AHST = AHSTall*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1939\n", + " elseif PMOT > AHSTaul, !- Program Line 1940\n", + " set AHST = AHSTaul*0.27+17.9+AHSToffset+AHSTtol, !- Program Line 1941\n", + " endif, !- Program Line 1942\n", + " else, !- Program Line 1943\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1944\n", + " set AHST = PMOT*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1945\n", + " elseif PMOT < AHSTall, !- Program Line 1946\n", + " set AHST = AHSTall*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1947\n", + " elseif PMOT > AHSTaul, !- Program Line 1948\n", + " set AHST = AHSTaul*0.53+10.3+AHSToffset+AHSTtol, !- Program Line 1949\n", + " endif, !- Program Line 1950\n", + " endif, !- Program Line 1951\n", + " endif, !- Program Line 1952\n", + " if (ComfStand == 20) && (ComfMod == 1), !- Program Line 1953\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1954\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1955\n", + " set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 1956\n", + " else, !- Program Line 1957\n", + " set ACST = 25+ACSTtol, !- Program Line 1958\n", + " endif, !- Program Line 1959\n", + " else, !- Program Line 1960\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1961\n", + " set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 1962\n", + " else, !- Program Line 1963\n", + " set ACST = 25+ACSTtol, !- Program Line 1964\n", + " endif, !- Program Line 1965\n", + " endif, !- Program Line 1966\n", + " endif, !- Program Line 1967\n", + " if (ComfStand == 20) && (ComfMod == 1), !- Program Line 1968\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1969\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1970\n", + " set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 1971\n", + " else, !- Program Line 1972\n", + " set AHST = 20+AHSTtol, !- Program Line 1973\n", + " endif, !- Program Line 1974\n", + " else, !- Program Line 1975\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 1976\n", + " set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 1977\n", + " else, !- Program Line 1978\n", + " set AHST = 20+AHSTtol, !- Program Line 1979\n", + " endif, !- Program Line 1980\n", + " endif, !- Program Line 1981\n", + " endif, !- Program Line 1982\n", + " if (ComfStand == 20) && (ComfMod == 2), !- Program Line 1983\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 1984\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 1985\n", + " set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 1986\n", + " elseif PMOT < ACSTall, !- Program Line 1987\n", + " if CAT == 90, !- Program Line 1988\n", + " set ACST = 24+ACSTtol, !- Program Line 1989\n", + " elseif CAT == 80, !- Program Line 1990\n", + " set ACST = 25+ACSTtol, !- Program Line 1991\n", + " endif, !- Program Line 1992\n", + " elseif PMOT > ACSTaul, !- Program Line 1993\n", + " if CAT == 90, !- Program Line 1994\n", + " set ACST = 26+ACSTtol, !- Program Line 1995\n", + " elseif CAT == 80, !- Program Line 1996\n", + " set ACST = 27+ACSTtol, !- Program Line 1997\n", + " endif, !- Program Line 1998\n", + " endif, !- Program Line 1999\n", + " else, !- Program Line 2000\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2001\n", + " set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2002\n", + " elseif PMOT < ACSTall, !- Program Line 2003\n", + " if CAT == 90, !- Program Line 2004\n", + " set ACST = 24+ACSTtol, !- Program Line 2005\n", + " elseif CAT == 80, !- Program Line 2006\n", + " set ACST = 25+ACSTtol, !- Program Line 2007\n", + " endif, !- Program Line 2008\n", + " elseif PMOT > ACSTaul, !- Program Line 2009\n", + " if CAT == 90, !- Program Line 2010\n", + " set ACST = 26+ACSTtol, !- Program Line 2011\n", + " elseif CAT == 80, !- Program Line 2012\n", + " set ACST = 27+ACSTtol, !- Program Line 2013\n", + " endif, !- Program Line 2014\n", + " endif, !- Program Line 2015\n", + " endif, !- Program Line 2016\n", + " endif, !- Program Line 2017\n", + " if (ComfStand == 20) && (ComfMod == 2), !- Program Line 2018\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2019\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2020\n", + " set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2021\n", + " elseif PMOT < AHSTall, !- Program Line 2022\n", + " if CAT == 90, !- Program Line 2023\n", + " set AHST = 20+AHSTtol, !- Program Line 2024\n", + " elseif CAT == 80, !- Program Line 2025\n", + " set AHST = 19+AHSTtol, !- Program Line 2026\n", + " endif, !- Program Line 2027\n", + " elseif PMOT > AHSTaul, !- Program Line 2028\n", + " if CAT == 90, !- Program Line 2029\n", + " set AHST = 23+AHSTtol, !- Program Line 2030\n", + " elseif CAT == 80, !- Program Line 2031\n", + " set AHST = 22+AHSTtol, !- Program Line 2032\n", + " endif, !- Program Line 2033\n", + " endif, !- Program Line 2034\n", + " else, !- Program Line 2035\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2036\n", + " set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2037\n", + " elseif PMOT < AHSTall, !- Program Line 2038\n", + " if CAT == 90, !- Program Line 2039\n", + " set AHST = 20+AHSTtol, !- Program Line 2040\n", + " elseif CAT == 80, !- Program Line 2041\n", + " set AHST = 19+AHSTtol, !- Program Line 2042\n", + " endif, !- Program Line 2043\n", + " elseif PMOT > AHSTaul, !- Program Line 2044\n", + " if CAT == 90, !- Program Line 2045\n", + " set AHST = 23+AHSTtol, !- Program Line 2046\n", + " elseif CAT == 80, !- Program Line 2047\n", + " set AHST = 22+AHSTtol, !- Program Line 2048\n", + " endif, !- Program Line 2049\n", + " endif, !- Program Line 2050\n", + " endif, !- Program Line 2051\n", + " endif, !- Program Line 2052\n", + " if (ComfStand == 20) && (ComfMod == 3), !- Program Line 2053\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2054\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2055\n", + " set ACST = PMOT*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2056\n", + " elseif PMOT < ACSTall, !- Program Line 2057\n", + " set ACST = ACSTall*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2058\n", + " elseif PMOT > ACSTaul, !- Program Line 2059\n", + " set ACST = ACSTaul*0.38+15.7+ACSToffset+ACSTtol, !- Program Line 2060\n", + " endif, !- Program Line 2061\n", + " else, !- Program Line 2062\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2063\n", + " set ACST = PMOT*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2064\n", + " elseif PMOT < ACSTall, !- Program Line 2065\n", + " set ACST = ACSTall*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2066\n", + " elseif PMOT > ACSTaul, !- Program Line 2067\n", + " set ACST = ACSTaul*0.47+9.07+ACSToffset+ACSTtol, !- Program Line 2068\n", + " endif, !- Program Line 2069\n", + " endif, !- Program Line 2070\n", + " endif, !- Program Line 2071\n", + " if (ComfStand == 20) && (ComfMod == 3), !- Program Line 2072\n", + " if (DayOfYear <= 121) || (DayOfYear > 295), !- Program Line 2073\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2074\n", + " set AHST = PMOT*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2075\n", + " elseif PMOT < AHSTall, !- Program Line 2076\n", + " set AHST = AHSTall*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2077\n", + " elseif PMOT > AHSTaul, !- Program Line 2078\n", + " set AHST = AHSTaul*0.38+15.7+AHSToffset+AHSTtol, !- Program Line 2079\n", + " endif, !- Program Line 2080\n", + " else, !- Program Line 2081\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2082\n", + " set AHST = PMOT*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2083\n", + " elseif PMOT < AHSTall, !- Program Line 2084\n", + " set AHST = AHSTall*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2085\n", + " elseif PMOT > AHSTaul, !- Program Line 2086\n", + " set AHST = AHSTaul*0.47+9.07+AHSToffset+AHSTtol, !- Program Line 2087\n", + " endif, !- Program Line 2088\n", + " endif, !- Program Line 2089\n", + " endif, !- Program Line 2090\n", + " if (ComfStand == 21) && (ComfMod == 2), !- Program Line 2091\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2092\n", + " set ACST = PMOT*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2093\n", + " elseif CAT==80, !- Program Line 2094\n", + " if PMOT < ACSTall, !- Program Line 2095\n", + " set ACST = 25+ACSTtol, !- Program Line 2096\n", + " elseif PMOT > ACSTaul, !- Program Line 2097\n", + " set ACST = 27+ACSTtol, !- Program Line 2098\n", + " endif, !- Program Line 2099\n", + " elseif CAT==90, !- Program Line 2100\n", + " if PMOT < ACSTall, !- Program Line 2101\n", + " set ACST = 24+ACSTtol, !- Program Line 2102\n", + " elseif PMOT > ACSTaul, !- Program Line 2103\n", + " set ACST = 26+ACSTtol, !- Program Line 2104\n", + " endif, !- Program Line 2105\n", + " endif, !- Program Line 2106\n", + " endif, !- Program Line 2107\n", + " if (ComfStand == 21) && (ComfMod == 2), !- Program Line 2108\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2109\n", + " set AHST = PMOT*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2110\n", + " elseif CAT==80, !- Program Line 2111\n", + " if PMOT < AHSTall, !- Program Line 2112\n", + " set AHST = 19+AHSTtol, !- Program Line 2113\n", + " elseif PMOT > AHSTaul, !- Program Line 2114\n", + " set AHST = 22+AHSTtol, !- Program Line 2115\n", + " endif, !- Program Line 2116\n", + " elseif CAT==90, !- Program Line 2117\n", + " if PMOT < AHSTall, !- Program Line 2118\n", + " set AHST = 20+AHSTtol, !- Program Line 2119\n", + " elseif PMOT > AHSTaul, !- Program Line 2120\n", + " set AHST = 23+AHSTtol, !- Program Line 2121\n", + " endif, !- Program Line 2122\n", + " endif, !- Program Line 2123\n", + " endif, !- Program Line 2124\n", + " if (ComfStand == 21) && (ComfMod == 3), !- Program Line 2125\n", + " if (PMOT >= ACSTall) && (PMOT <= ACSTaul), !- Program Line 2126\n", + " set ACST = PMOT*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2127\n", + " elseif PMOT < ACSTall, !- Program Line 2128\n", + " set ACST = ACSTall*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2129\n", + " elseif PMOT > ACSTaul, !- Program Line 2130\n", + " set ACST = ACSTaul*0.678+13.51+ACSToffset+ACSTtol, !- Program Line 2131\n", + " endif, !- Program Line 2132\n", + " endif, !- Program Line 2133\n", + " if (ComfStand == 21) && (ComfMod == 3), !- Program Line 2134\n", + " if (PMOT >= AHSTall) && (PMOT <= AHSTaul), !- Program Line 2135\n", + " set AHST = PMOT*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2136\n", + " elseif PMOT < AHSTall, !- Program Line 2137\n", + " set AHST = AHSTall*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2138\n", + " elseif PMOT > AHSTaul, !- Program Line 2139\n", + " set AHST = AHSTaul*0.678+13.51+AHSToffset+AHSTtol, !- Program Line 2140\n", + " endif, !- Program Line 2141\n", + " endif, !- Program Line 2142\n", + " if (ComfStand == 22), !- Program Line 2143\n", + " if CoolingSeason == 1, !- Program Line 2144\n", + " if (CAT==3), !- Program Line 2145\n", + " set ACST = 24.5+2.5+ACSTtol, !- Program Line 2146\n", + " elseif (CAT==2), !- Program Line 2147\n", + " set ACST = 24.5+1.5+ACSTtol, !- Program Line 2148\n", + " elseif (CAT==1), !- Program Line 2149\n", + " set ACST = 24.5+1+ACSTtol, !- Program Line 2150\n", + " endif, !- Program Line 2151\n", + " else, !- Program Line 2152\n", + " if (CAT==3), !- Program Line 2153\n", + " set ACST = 22+3+ACSTtol, !- Program Line 2154\n", + " elseif (CAT==2), !- Program Line 2155\n", + " set ACST = 22+2+ACSTtol, !- Program Line 2156\n", + " elseif (CAT==1), !- Program Line 2157\n", + " set ACST = 22+1+ACSTtol, !- Program Line 2158\n", + " endif, !- Program Line 2159\n", + " endif, !- Program Line 2160\n", + " endif, !- Program Line 2161\n", + " if (ComfStand == 22), !- Program Line 2162\n", + " if CoolingSeason == 1, !- Program Line 2163\n", + " if (CAT==3), !- Program Line 2164\n", + " set AHST = 24.5-2.5+AHSTtol, !- Program Line 2165\n", + " elseif (CAT==2), !- Program Line 2166\n", + " set AHST = 24.5-1.5+AHSTtol, !- Program Line 2167\n", + " elseif (CAT==1), !- Program Line 2168\n", + " set AHST = 24.5-1+AHSTtol, !- Program Line 2169\n", + " endif, !- Program Line 2170\n", + " else, !- Program Line 2171\n", + " if (CAT==3), !- Program Line 2172\n", + " set AHST = 22-3+AHSTtol, !- Program Line 2173\n", + " elseif (CAT==2), !- Program Line 2174\n", + " set AHST = 22-2+AHSTtol, !- Program Line 2175\n", + " elseif (CAT==1), !- Program Line 2176\n", + " set AHST = 22-1+AHSTtol, !- Program Line 2177\n", + " endif, !- Program Line 2178\n", + " endif, !- Program Line 2179\n", + " endif, !- Program Line 2180\n", + " set ACSTx2 = ACST*SetpointAcc, !- Program Line 2181\n", + " set AHSTx2 = AHST*SetpointAcc, !- Program Line 2182\n", + " set roundedACSTx2 = @Round ACSTx2, !- Program Line 2183\n", + " set roundedAHSTx2 = @Round AHSTx2, !- Program Line 2184\n", + " if roundedACSTx2 - ACSTx2 < 0, !- Program Line 2185\n", + " set ACSTroundedUp = 0, !- Program Line 2186\n", + " else, !- Program Line 2187\n", + " set ACSTroundedUp = 1, !- Program Line 2188\n", + " endif, !- Program Line 2189\n", + " if roundedAHSTx2 - AHSTx2 < 0, !- Program Line 2190\n", + " set AHSTroundedUp = 0, !- Program Line 2191\n", + " else, !- Program Line 2192\n", + " set AHSTroundedUp = 1, !- Program Line 2193\n", + " endif, !- Program Line 2194\n", + " if ACSTroundedUp == 0, !- Program Line 2195\n", + " set roundedACST = roundedACSTx2 / SetpointAcc, !- Program Line 2196\n", + " else, !- Program Line 2197\n", + " set roundedACST = (roundedACSTx2 - 1) / SetpointAcc, !- Program Line 2198\n", + " endif, !- Program Line 2199\n", + " if AHSTroundedUp == 0, !- Program Line 2200\n", + " set roundedAHST = (roundedAHSTx2 + 1) / SetpointAcc, !- Program Line 2201\n", + " else, !- Program Line 2202\n", + " set roundedAHST = roundedAHSTx2 / SetpointAcc, !- Program Line 2203\n", + " endif; !- Program Line 2204]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setast']\n" + ] + }, + { + "cell_type": "markdown", + "id": "b2e1e8a5-4af9-4d23-82b9-af45b49ba26d", + "metadata": {}, + "source": [ + "If you want to inspect the `Parameter` objects, you can see the internal variable `parameters_list`:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4b72a5b5-417f-410b-b30a-bf6550dbb364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_m', min=0.01, max=0.99)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_n', min=5, max=23)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASToffset', min=2, max=4)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTall', min=10, max=15)]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTaul', min=30, max=35)])]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_list" + ] + }, + { + "cell_type": "markdown", + "id": "16b3b170-683d-49b3-9d6b-4d3772245647", + "metadata": {}, + "source": [ + "## Running the parametric simulation" + ] + }, + { + "cell_type": "markdown", + "id": "e36956e3-781b-4fe1-980f-ad9fbca21fba", + "metadata": {}, + "source": [ + "### Setting the problem" + ] + }, + { + "cell_type": "markdown", + "id": "60150ec0-8c63-4097-bd4f-c8263dc22d47", + "metadata": {}, + "source": [ + "First, let's set the problem. To do so, use the `set_problem()` method. In case of the parametric simulation you don't need to input any argument. However, in case of the optimisation, you must input the arguments `minimize_outputs`, `constraints` and `constraint_bounds`, similarly as you would do in the besos `EPProblem` class." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "cd2c3ebc-99ea-46a2-94cf-37b3ddd77630", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_problem()" + ] + }, + { + "cell_type": "markdown", + "id": "cc1eac32-9784-441f-98e3-0382053ba14d", + "metadata": {}, + "source": [ + "Again, you can inspect the `EPProblem` class instance in the internal variable `problem`:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a4f80f41-febc-47e0-b610-8c07893a5c20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_m', min=0.01, max=0.99)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_n', min=5, max=23)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASToffset', min=2, max=4)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTall', min=10, max=15)]), Parameter(selector=GenericSelector(set=), value_descriptors=[RangeParameter(name='CustAST_ASTaul', min=30, max=35)])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.problem" + ] + }, + { + "cell_type": "markdown", + "id": "901b319a-2ef8-4c7c-9fea-7512b81807f0", + "metadata": {}, + "source": [ + "### Sampling the simulation runs" + ] + }, + { + "cell_type": "markdown", + "id": "cecbc846-7227-498a-b0f8-f8795269df25", + "metadata": {}, + "source": [ + "The way to inform besos of the variations and permutations it must carry out in the parametric analysis is by means of a DataFrame, which must contain a column per `Parameter`, in which values are specified. There are multiple ways to do this DataFrame. For instance, we could make a dataframe from scratch:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "30f78385-766d-47f2-b394-c922f8403b30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CustAST_m CustAST_n CustAST_ASToffset CustAST_ASTall CustAST_ASTaul\n", + "0 0.1 22 2.5 10 35\n", + "1 0.6 8 4.0 10 35" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "param_dict = {\n", + " 'CustAST_m': [0.1, 0.6], \n", + " 'CustAST_n': [22, 8], \n", + " 'CustAST_ASToffset': [2.5, 4],\n", + " 'CustAST_ASTall': [10, 10],\n", + " 'CustAST_ASTaul': [35, 35],\n", + "}\n", + "input_param_df = pd.DataFrame(data=param_dict)\n", + "input_param_df" + ] + }, + { + "cell_type": "markdown", + "id": "242f6e72-de2e-4fa7-8265-f9d154e198f9", + "metadata": {}, + "source": [ + "We could input that df, which would result in 2 simulations. But now, imagine we want to make all possible combinations from the values we just did from scratch. We could use the function `make_all_combinations(parameters_values_dict)`. The argument `parameters_values_dict` must be a dictionary in the format {'parameter name': list_of_values}, such as the previouly defined param_dict, so let's input it. Let's see the possibilities:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "0f6fcf93-64d2-4a8f-9740-0e329c1dccfd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfMod
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" + ], + "text/plain": [ + " ComfStand CAT ComfMod\n", + "2 2 80 3\n", + "3 2 90 3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_values_df" + ] + }, + { + "cell_type": "markdown", + "id": "c0c976c6-eed1-4727-a274-975585466130", + "metadata": {}, + "source": [ + "### Running the simulations" + ] + }, + { + "cell_type": "markdown", + "id": "302f66b3-55ba-468b-91bb-d0c7a53f4387", + "metadata": {}, + "source": [ + "Now, we're ready to run the simulations, by means of the `run_parametric_simulation(epws, out_dir, df, processes)` method. After calling the method, the outputs (a DataFrame) is saved in the internal variable `outputs_param_simulation`. It is based on the use of the `EvaluatorEP` class, `df_apply` method. We want to run the parametric simulations with both Sydney and Seville climate files, therefore the filenames are input in a list in the `epws` argument. The simulation outputs will be saved in a directory named 'notebook_temp_dir'. The values for the parameters will be driven by the internal variable `parameters_values_df`, as input in the `df` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5e5a19ab-d2a3-44ac-9d2d-01672ac8db08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n" + ] + } + ], + "source": [ + "parametric.run_parametric_simulation(\n", + " epws=['Sydney.epw', 'Seville.epw'],\n", + " out_dir='notebook_temp_dir',\n", + " df=parametric.parameters_values_df,\n", + " #processes=6, # The number of CPUs to be used. Default is 2.\n", + " #keep_input=True, # To keep the input values of parameters, as entered in df argument. Default is True.\n", + " #keep_dirs=True # To keep the simulation outputs. Default is True.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8f768acb-e813-4d4d-91d7-7b96a98ecaad", + "metadata": {}, + "source": [ + "Let's take a look at the simulation results" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9461f205-acd1-4532-b691-9dd7d9778873", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexComfStandCATComfModHeating:ElectricityCooling:ElectricityElectricity:HVACAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time seriesoutput_direpw
0228032.593138e+091.754898e+102.735351e+10[27.325804398148147, 27.91096527777778, 27.910...[20.325804398148147, 20.910965277777777, 20.91...[23.771739074031462, 23.897364637263966, 23.75...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8...Sydney
1329033.639536e+093.621666e+104.728630e+10[26.325804398148147, 26.91096527777778, 26.910...[21.325804398148147, 21.910965277777777, 21.91...[23.66668667903001, 23.8347876559133, 23.82938...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH...Sydney
2228032.062257e+093.171532e+104.054615e+10[24.785096064814812, 24.86211527777778, 24.862...[17.785096064814812, 17.86211527777778, 17.862...[21.151988618198942, 21.208271993109328, 20.26...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR...Seville
3329032.297988e+094.516874e+105.445162e+10[23.785096064814812, 23.86211527777778, 23.862...[18.785096064814812, 18.86211527777778, 18.862...[22.631374958463137, 20.71719912972282, 20.492...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H...Seville
\n", + "
" + ], + "text/plain": [ + " index ComfStand CAT ComfMod Heating:Electricity Cooling:Electricity \\\n", + "0 2 2 80 3 2.593138e+09 1.754898e+10 \n", + "1 3 2 90 3 3.639536e+09 3.621666e+10 \n", + "2 2 2 80 3 2.062257e+09 3.171532e+10 \n", + "3 3 2 90 3 2.297988e+09 4.516874e+10 \n", + "\n", + " Electricity:HVAC \\\n", + "0 2.735351e+10 \n", + "1 4.728630e+10 \n", + "2 4.054615e+10 \n", + "3 5.445162e+10 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 [27.325804398148147, 27.91096527777778, 27.910... \n", + "1 [26.325804398148147, 26.91096527777778, 26.910... \n", + "2 [24.785096064814812, 24.86211527777778, 24.862... \n", + "3 [23.785096064814812, 23.86211527777778, 23.862... \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 [20.325804398148147, 20.910965277777777, 20.91... \n", + "1 [21.325804398148147, 21.910965277777777, 21.91... \n", + "2 [17.785096064814812, 17.86211527777778, 17.862... \n", + "3 [18.785096064814812, 18.86211527777778, 18.862... \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 [23.771739074031462, 23.897364637263966, 23.75... \n", + "1 [23.66668667903001, 23.8347876559133, 23.82938... \n", + "2 [21.151988618198942, 21.208271993109328, 20.26... \n", + "3 [22.631374958463137, 20.71719912972282, 20.492... \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \\\n", + "0 [21.325694444444437, 21.325694444444437, 21.32... \n", + "1 [21.325694444444437, 21.325694444444437, 21.32... \n", + "2 [11.490694444444445, 11.490694444444445, 11.49... \n", + "3 [11.490694444444445, 11.490694444444445, 11.49... \n", + "\n", + " output_dir epw \n", + "0 notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8... Sydney \n", + "1 notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH... Sydney \n", + "2 notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR... Seville \n", + "3 notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H... Seville " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.outputs_param_simulation" + ] + }, + { + "cell_type": "markdown", + "id": "efa37b3d-45f9-4bbd-b2c2-3f36b743ee0b", + "metadata": {}, + "source": [ + "We can see the columns are the following:" + ] + }, + { + "cell_type": "markdown", + "id": "907ca41e-e151-4e90-a37b-ed0b596e1852", + "metadata": {}, + "source": [ + "- the parameters, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c733f7a2-fd24-4b14-9ab1-98075d261401", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand', 'CAT', 'ComfMod']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.value_descriptors[0].name for i in parametric.parameters_list]" + ] + }, + { + "cell_type": "markdown", + "id": "c30f391f-08a0-488e-8dd3-67aa9189790c", + "metadata": {}, + "source": [ + "- the outputs, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "405d7408-599d-4493-a497-5bfe686db381", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + " 'Adaptive Cooling Setpoint Temperature_No Tolerance_time series',\n", + " 'Adaptive Heating Setpoint Temperature_No Tolerance_time series',\n", + " 'Zone Operative Temperature_time series',\n", + " 'Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.name for i in parametric.sim_outputs]" + ] + }, + { + "cell_type": "markdown", + "id": "cf172d66-6883-481c-9779-a3382616924e", + "metadata": {}, + "source": [ + "- the path to the output files for each simulation, in the column 'out_dir'\n", + "- the epw for each simulation, in the column 'epw'" + ] + }, + { + "cell_type": "markdown", + "id": "e2f6302e-8105-482b-a984-c4bc03ac8086", + "metadata": {}, + "source": [ + "### Visualising the results" + ] + }, + { + "cell_type": "markdown", + "id": "8de1cbd1-8cf0-41c4-b394-6cf8481f2edd", + "metadata": {}, + "source": [ + "#### Aggregated columns" + ] + }, + { + "cell_type": "markdown", + "id": "d7b2ad11-4f2b-46d0-8a59-3e7e842b2ae9", + "metadata": {}, + "source": [ + "At this point, if you have some knowledge of pandas and some package to plot the data (e.g. matplotlib or seaborn), you can carry out your own analysis and visualization. We're going to do some example below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3b08c431-5c10-47bc-b8d9-bbed174fb35c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(\n", + " data=parametric.outputs_param_simulation,\n", + " x='Heating:Electricity',\n", + " y='Cooling:Electricity',\n", + " hue='CAT',\n", + " style='epw'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "710213aa-c8d2-4565-b121-a0db80753b52", + "metadata": {}, + "source": [ + "#### Time series columns" + ] + }, + { + "cell_type": "markdown", + "id": "d66994c0-7514-456f-8d9c-2c3acce6e656", + "metadata": {}, + "source": [ + "If you requested some output to be reported in time series, you can get a dataframe containing the hourly values using the method `get_hourly_df()`. This dataframe is saved in the internal variable `outputs_param_simulation_hourly`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "74848927-55f4-4e27-ae75-bf66baf49ba7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfModepwhourdatetimeAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series
02803Sydney12024-01-01 01:00:0027.32580420.32580423.77173921.325694
12803Sydney22024-01-01 02:00:0027.91096520.91096523.89736521.325694
22803Sydney32024-01-01 03:00:0027.91096520.91096523.75949921.325694
32803Sydney42024-01-01 04:00:0027.91096520.91096523.81012121.325694
42803Sydney52024-01-01 05:00:0027.91096520.91096524.11513121.325694
.................................
350352903Seville87562024-12-30 20:00:0023.93343018.93343023.61795711.720741
350362903Seville87572024-12-30 21:00:0023.93343018.93343023.57101211.720741
350372903Seville87582024-12-30 22:00:0023.93343018.93343023.34048011.720741
350382903Seville87592024-12-30 23:00:0023.93343018.93343023.06050111.720741
350392903Seville87602024-12-31 00:00:0023.93343018.93343022.83835211.720741
\n", + "

35040 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " ComfStand CAT ComfMod epw hour datetime \\\n", + "0 2 80 3 Sydney 1 2024-01-01 01:00:00 \n", + "1 2 80 3 Sydney 2 2024-01-01 02:00:00 \n", + "2 2 80 3 Sydney 3 2024-01-01 03:00:00 \n", + "3 2 80 3 Sydney 4 2024-01-01 04:00:00 \n", + "4 2 80 3 Sydney 5 2024-01-01 05:00:00 \n", + "... ... ... ... ... ... ... \n", + "35035 2 90 3 Seville 8756 2024-12-30 20:00:00 \n", + "35036 2 90 3 Seville 8757 2024-12-30 21:00:00 \n", + "35037 2 90 3 Seville 8758 2024-12-30 22:00:00 \n", + "35038 2 90 3 Seville 8759 2024-12-30 23:00:00 \n", + "35039 2 90 3 Seville 8760 2024-12-31 00:00:00 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 27.325804 \n", + "1 27.910965 \n", + "2 27.910965 \n", + "3 27.910965 \n", + "4 27.910965 \n", + "... ... \n", + "35035 23.933430 \n", + "35036 23.933430 \n", + "35037 23.933430 \n", + "35038 23.933430 \n", + "35039 23.933430 \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 20.325804 \n", + "1 20.910965 \n", + "2 20.910965 \n", + "3 20.910965 \n", + "4 20.910965 \n", + "... ... \n", + "35035 18.933430 \n", + "35036 18.933430 \n", + "35037 18.933430 \n", + "35038 18.933430 \n", + "35039 18.933430 \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 23.771739 \n", + "1 23.897365 \n", + "2 23.759499 \n", + "3 23.810121 \n", + "4 24.115131 \n", + "... ... \n", + "35035 23.617957 \n", + "35036 23.571012 \n", + "35037 23.340480 \n", + "35038 23.060501 \n", + "35039 22.838352 \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \n", + "0 21.325694 \n", + "1 21.325694 \n", + "2 21.325694 \n", + "3 21.325694 \n", + "4 21.325694 \n", + "... ... \n", + "35035 11.720741 \n", + "35036 11.720741 \n", + "35037 11.720741 \n", + "35038 11.720741 \n", + "35039 11.720741 \n", + "\n", + "[35040 rows x 10 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.get_hourly_df()\n", + "parametric.outputs_param_simulation_hourly" + ] + }, + { + "cell_type": "markdown", + "id": "dbac2c53-1b49-43a5-b8c7-02877ec8b4aa", + "metadata": {}, + "source": [ + "Let's prepare the `outputs_param_simulation_hourly` df for plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "921324d1-f102-4bd3-b43e-340e435bdca0", + "metadata": {}, + "outputs": [], + "source": [ + "#Let's make a copy of the dataframe to not to modify the original one\n", + "df = parametric.outputs_param_simulation_hourly.copy()\n", + "\n", + "# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot:\n", + "rmot = [i for i in df.columns if 'Running Average' in i][0]\n", + "\n", + "#Let's remove the columns where value is the same for all rows\n", + "for c in df.columns:\n", + " if len(set(df[c])) == 1:\n", + " df = df.drop(columns=[c])\n", + "#Now let's remove the hour and datetime columns, since will\n", + "df = df.drop(columns=['hour'])\n", + "\n", + "# Now let's reshape the df for plotting purposes\n", + "df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4aa2a50-88ee-4c66-ab3a-48a26eaec9c1", + "metadata": {}, + "source": [ + "Firstly, we're going to plot the hourly temperatures to see the slope of the comfort model, and check all hours are within thermal comfort limits (since we applied adaptive setpoints)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2213ebd0-2f3c-4e74-bd06-117ec72c3108", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.scatterplot,\n", + " x=rmot,\n", + " y='value',\n", + " hue='variable',\n", + " s=1,\n", + " #alpha=0.5\n", + ")\n", + "g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "\n", + "for lh in g._legend.legend_handles:\n", + " lh.set_markersize(5)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "14dfbd82-2e4f-45b6-96ca-da72265e117c", + "metadata": {}, + "source": [ + "Now, we're going to plot time on x-axis and change the plot type to lineplot, to see the variarion of the indoor operative temperature throughout the year:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b673ca88-bbd8-4c89-989e-7cf7c613e90c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.lineplot,\n", + " x='datetime',\n", + " y='value',\n", + " hue='variable',\n", + ")\n", + "g.set_axis_labels('Time', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0b253ef-b8ee-402b-a741-b4b89181e589", + "metadata": {}, + "source": [ + "We're done with the example, so let's remove all new files, so that we can re-run it again." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "abefda96-72f4-4e8a-8d2a-5337647c480b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'available_outputs', 'notebook_temp_dir'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_files = [i for i in listdir()]\n", + "new_files = set(current_files) - set(original_files)\n", + "new_files" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f9dbc462-7f2b-40e2-b7b0-41e9243b3b59", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "for item in new_files:\n", + " item_path = os.path.join(os.getcwd(), item)\n", + " if os.path.isfile(item_path):\n", + " os.remove(item_path)\n", + " elif os.path.isdir(item_path):\n", + " shutil.rmtree(item_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/param_sim_accim_predefined_model.ipynb b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/param_sim_accim_predefined_model.ipynb new file mode 100644 index 0000000..cb2d444 --- /dev/null +++ b/accim/sample_files/jupyter_notebooks/parametric_and_optimisation/param_sim_accim_predefined_model.ipynb @@ -0,0 +1,3069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2cf45ee0-4144-4034-99a9-311270c74d30", + "metadata": {}, + "source": [ + "# Parametric simulation using accim predefined models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f71c6322-10f1-4fde-bc8d-358163b8619b", + "metadata": {}, + "outputs": [], + "source": [ + "#todo import qgrid to manually change output dfs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a8361222-b96d-4121-9560-1480e3ee4f44", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import accim\n", + "from accim.parametric_and_optimisation.objectives import return_time_series\n", + "from besos import eppy_funcs as ef\n", + "from matplotlib import pyplot as plt\n", + "import seaborn as sns\n", + "from accim.utils import print_available_outputs_mod\n", + "from accim.parametric_and_optimisation.main import OptimParamSimulation, get_rdd_file_as_df, get_mdd_file_as_df, parse_mtd_file\n", + "from os import listdir\n" + ] + }, + { + "cell_type": "markdown", + "id": "c699ceba-f765-47c5-b5fd-b0cee5522b3a", + "metadata": {}, + "source": [ + "Let's have a look at the files we currently have in the path:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "233674c3-f15d-4917-aa47-430ae91486d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['.ipynb_checkpoints',\n", + " 'accim_predefined_model.ipynb',\n", + " 'Seville.epw',\n", + " 'Sydney.epw',\n", + " 'TestModel.idf',\n", + " '__init__.py']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "original_files = [i for i in listdir()]\n", + "original_files" + ] + }, + { + "cell_type": "markdown", + "id": "16e31d87-3a87-40c1-aeef-d6ea1171291a", + "metadata": {}, + "source": [ + "Firstly, the IDF must be read using besos's `get_building` function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d6e13f6-7466-4e96-a559-8c771895c1ac", + "metadata": {}, + "outputs": [], + "source": [ + "building = ef.get_building('TestModel.idf')" + ] + }, + { + "cell_type": "markdown", + "id": "affcedd2-3206-4178-bb93-3ae151e04301", + "metadata": {}, + "source": [ + "For this analysis, we want to use the HVAC system in all hours of the year, so that temperature is always comfortable. Therefore, we are going to set the occupancy to always on by means of the function `accim.utils.set_occupancy_to_always`, in which we input the IDF class instance we read in the previous cell." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f5c2feb1-ca1e-4b8a-bc50-1f116cca9fa4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On 24/7 Schedule:Compact object was already in the model.\n", + "People Block1:Zone2 Number of People Schedule Name has been set to always occupied.\n", + "People Block1:Zone1 Number of People Schedule Name has been set to always occupied.\n" + ] + } + ], + "source": [ + "accim.utils.set_occupancy_to_always(idf_object=building)" + ] + }, + { + "cell_type": "markdown", + "id": "29827b0f-9086-4e26-873b-db2f7818a18a", + "metadata": {}, + "source": [ + "Now, let's start with the settings for the parametric analysis. First, let's instantiate the class `OptimParamSimulation`, and let's pass the IDF instance in the argument `building`. Argument `parameters_type` can take 3 different strings:\n", + "- \"accim predefined model\", in which models are those previously defined in accim (ComfStand=0 to ComfStand=22);\n", + "- \"accim custom model\", in which key parameters of the adaptive comfort model are defined in the relevant arguments;\n", + "- \"apmv setpoints\", in which setpoints are based on the aPMV (Adaptive Predicted Mean Vote) instead of the PMV index;\n", + "\n", + "In this case, we're going to use the 'accim predefined model' type, in which the models we can use are those already defined in accim." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2a5fa261-bfee-4b08-a8fe-c62f46a481eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------\n", + "Adaptive-Comfort-Control-Implemented Model (ACCIM) v0.7.5\n", + "--------------------------------------------------------\n", + "\n", + "This tool allows to apply adaptive setpoint temperatures. \n", + "For further information, please read the documentation: \n", + "https://accim.readthedocs.io/en/master/\n", + "For a visual understanding of the tool, please visit the following jupyter notebooks:\n", + "- Using addAccis() to apply adaptive setpoint temperatures\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/addAccis/using_addAccis.html\n", + "- Using rename_epw_files() to rename the EPWs for proper data analysis after simulation\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/rename_epw_files/using_rename_epw_files.html\n", + "- Using runEp() to directly run simulations with EnergyPlus\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/runEp/using_runEp.html\n", + "- Using the class Table() for data analysis\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/Table/using_Table.html\n", + "- Full example\n", + "https://accim.readthedocs.io/en/master/jupyter_notebooks/full_example/full_example.html\n", + "\n", + "Starting with the process.\n", + "Basic input data:\n", + "ScriptType is: vrf_mm\n", + "Supply Air Temperature Input Method is: temperature difference\n", + "Output type is: standard\n", + "Output frequencies are: \n", + "['hourly']\n", + "EnergyPlus version is: 23.1\n", + "Temperature Control method is: temperature\n", + "\n", + "=======================START OF GENERIC IDF FILE GENERATION PROCESS=======================\n", + "\n", + "Starting with file:\n", + "The occupied zones in the model are:\n", + "BLOCK1:ZONE2\n", + "BLOCK1:ZONE1\n", + "The windows and doors in the model are:\n", + "Block1_Zone2_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_4_0_0_0_0_0_Win\n", + "Block1_Zone2_Wall_5_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_2_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_3_0_0_0_0_0_Win\n", + "Block1_Zone1_Wall_5_0_0_0_0_0_Win\n", + "The zones in the model are:\n", + "BLOCK1_ZONE2\n", + "BLOCK1_ZONE1\n", + "The people objects in the model have been amended.\n", + "BLOCK1:ZONE2 Thermostat has been added\n", + "BLOCK1:ZONE1 Thermostat has been added\n", + "On Schedule already was in the model\n", + "TypOperativeTempControlSch Schedule already was in the model\n", + "All ZoneHVAC:IdealLoadsAirSystem Heating and Cooling availability schedules has been set to on\n", + "On 24/7 Schedule already was in the model\n", + "Control type schedule: Always 4 Schedule has been added\n", + "Relative humidity setpoint schedule: Always 50.00 Schedule has been added\n", + "Heating Fanger comfort setpoint: Always -0.5 Schedule has been added\n", + "Cooling Fanger comfort setpoint: Always 0.1 Schedule has been added\n", + "Zone CO2 setpoint: Always 900ppm Schedule has been added\n", + "Min CO2 concentration: Always 600ppm Schedule has been added\n", + "Generic contaminant setpoint: Always 0.5ppm Schedule has been added\n", + "Air distribution effectiveness (always 1) Schedule has been added\n", + "VRF Heating Cooling (Northern Hemisphere) Schedule has been added\n", + "DefaultFanEffRatioCurve Curve:Cubic Object has been added\n", + "VRFTUCoolCapFT Curve:Cubic Object has been added\n", + "VRFTUHeatCapFT Curve:Cubic Object has been added\n", + "VRFCoolCapFTBoundary Curve:Cubic Object has been added\n", + "VRFCoolEIRFTBoundary Curve:Cubic Object has been added\n", + "CoolingEIRLowPLR Curve:Cubic Object has been added\n", + "VRFHeatCapFTBoundary Curve:Cubic Object has been added\n", + "VRFHeatEIRFTBoundary Curve:Cubic Object has been added\n", + "HeatingEIRLowPLR Curve:Cubic Object has been added\n", + "DefaultFanPowerRatioCurve Curve:Exponent Object has been added\n", + "DXHtgCoilDefrostEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFT Curve:Biquadratic Object has been added\n", + "VRFCoolCapFTHi Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFT Curve:Biquadratic Object has been added\n", + "VRFCoolEIRFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatCapFT Curve:Biquadratic Object has been added\n", + "VRFHeatCapFTHi Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFT Curve:Biquadratic Object has been added\n", + "VRFHeatEIRFTHi Curve:Biquadratic Object has been added\n", + "CoolingLengthCorrectionFactor Curve:Biquadratic Object has been added\n", + "VRF Piping Correction Factor for Length in Heating Mode Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Cooling Energy Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Capacity Modifier Curve:Biquadratic Object has been added\n", + "VRF Heat Recovery Heating Energy Modifier Curve:Biquadratic Object has been added\n", + "VRFACCoolCapFFF Curve:Quadratic Object has been added\n", + "CoolingEIRHiPLR Curve:Quadratic Object has been added\n", + "VRFCPLFFPLR Curve:Quadratic Object has been added\n", + "HeatingEIRHiPLR Curve:Quadratic Object has been added\n", + "CoolingCombRatio Curve:Linear Object has been added\n", + "HeatingCombRatio Curve:Linear Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 AirConditioner:VariableRefrigerantFlow Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE2 Zone List Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Outdoor Air Node Object has been added\n", + "VRF Outdoor Unit_BLOCK1:ZONE1 Zone List Object has been added\n", + "BLOCK1:ZONE2 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE1 Sizing:Zone Object has been added\n", + "BLOCK1:ZONE2 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE1 Design Specification Outdoor Air Object has been added\n", + "BLOCK1:ZONE2 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE1 Design Specification Zone Air Distribution Object has been added\n", + "BLOCK1:ZONE2 Nodelist Objects has been added\n", + "BLOCK1:ZONE1 Nodelist Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentConnections Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:EquipmentList Objects has been added\n", + "BLOCK1:ZONE2 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 ZoneHVAC:TerminalUnit:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Cooling:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE1 Coil:Heating:DX:VariableRefrigerantFlow Object has been added\n", + "BLOCK1:ZONE2 Fan:ConstantVolume Object has been added\n", + "BLOCK1:ZONE1 Fan:ConstantVolume Object has been added\n", + "Vent_SP_temp Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE2 Schedule has been added\n", + "AHST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "ACST_Sch_BLOCK1_ZONE1 Schedule has been added\n", + "Added - SetComfTemp Program\n", + "Added - CountHours_BLOCK1_ZONE2 Program\n", + "Added - CountHours_BLOCK1_ZONE1 Program\n", + "Added - SetAppLimits Program\n", + "Added - ApplyCAT Program\n", + "Added - SetAST Program\n", + "Added - SetASTnoTol Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program\n", + "Added - SetInputData Program\n", + "Added - SetVOFinputData Program\n", + "Added - SetVST Program\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program\n", + "Added - Comfort Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature Output Variable\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Output Variable\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Output Variable\n", + "Added - z_test_ComfStand Output Variable\n", + "Added - z_test_ACSTaul Output Variable\n", + "Added - z_test_ACSTall Output Variable\n", + "Added - z_test_AHSTaul Output Variable\n", + "Added - z_test_AHSTall Output Variable\n", + "Added - z_test_CAT Output Variable\n", + "Added - z_test_ACSToffset Output Variable\n", + "Added - z_test_AHSToffset Output Variable\n", + "Added - z_test_ComfMod Output Variable\n", + "Added - z_test_ACSTtol Output Variable\n", + "Added - z_test_SetpointAcc Output Variable\n", + "Added - z_test_CustAST_m Output Variable\n", + "Added - z_test_CustAST_n Output Variable\n", + "Added - z_test_AHSTtol Output Variable\n", + "Added - Ventilation Setpoint Temperature Output Variable\n", + "Added - Minimum Outdoor Temperature for ventilation Output Variable\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Output Variable\n", + "Added - Multiplier for Ventilation Opening Factor Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Output Variable\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Output Variable\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Output Variable\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Output Variable\n", + "Global variables objects have been added\n", + "Internal variables objects have been added\n", + "Added - RMOT Sensor\n", + "Added - PMOT Sensor\n", + "Added - Occ_count_BLOCK1_ZONE2 Sensor\n", + "Added - Occ_count_BLOCK1_ZONE1 Sensor\n", + "Added - BLOCK1_ZONE2_OpT Sensor\n", + "Added - BLOCK1_ZONE2_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE2_OutT Sensor\n", + "Added - BLOCK1_ZONE1_OpT Sensor\n", + "Added - BLOCK1_ZONE1_WindSpeed Sensor\n", + "Added - BLOCK1_ZONE1_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OpT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_WindSpeed Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_OutT Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_Occ_count Sensor\n", + "Added - OutT Sensor\n", + "Added - AHST_Act_BLOCK1_ZONE2 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE2 Actuator\n", + "Added - AHST_Act_BLOCK1_ZONE1 Actuator\n", + "Added - ACST_Act_BLOCK1:ZONE1 Actuator\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_VentOpenFact Actuator\n", + "Added - BLOCK1_ZONE2_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE2_HeatCoil Sensor\n", + "Added - BLOCK1_ZONE1_CoolCoil Sensor\n", + "Added - BLOCK1_ZONE1_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_4_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone2_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_2_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_3_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_CoolCoil Sensor\n", + "Added - Block1_Zone1_Wall_5_0_0_0_0_0_Win_HeatCoil Sensor\n", + "Added - SetComfTemp Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHours_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetAppLimits Program Calling Manager\n", + "Added - ApplyCAT Program Calling Manager\n", + "Added - SetAST Program Calling Manager\n", + "Added - SetASTnoTol Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE2 Program Calling Manager\n", + "Added - CountHoursNoApp_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetGeoVarBLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetInputData Program Calling Manager\n", + "Added - SetVOFinputData Program Calling Manager\n", + "Added - SetVST Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE2 Program Calling Manager\n", + "Added - ApplyAST_BLOCK1_ZONE1 Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_4_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone2_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_2_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_3_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetMyVOF_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - SetWindowOperation_Block1_Zone1_Wall_5_0_0_0_0_0_Win Program Calling Manager\n", + "Added - Comfort Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Cooling Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - Adaptive Heating Setpoint Temperature_No Tolerance Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfStand Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTaul Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTall Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CAT Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSToffset Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ComfMod Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_ACSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_SetpointAcc Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_m Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_CustAST_n Reporting FrequencyHourly Output:Variable data\n", + "Added - z_test_AHSTtol Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Setpoint Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Minimum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Maximum Outdoor Temperature Difference for ventilation Reporting FrequencyHourly Output:Variable data\n", + "Added - Multiplier for Ventilation Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Comfortable Hours_Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Comfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Discomfortable Hours_No Applicability_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Occupied Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Hot Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Discomfortable Non Applicable Cold Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Floor Area_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Air Volume_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - People Occupant Count_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - Ventilation Hours_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermostat Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Operative Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort CEN 15251 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Cooling Coil Total Cooling Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Heating Coil Heating Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electric Demand Power Reporting FrequencyHourly Output:Variable data\n", + "Added - Facility Total HVAC Electricity Demand Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Surface Venting Window or Door Opening Factor Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Infiltration Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Air Change Rate Reporting FrequencyHourly Output:Variable data\n", + "Added - AFN Zone Ventilation Volume Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Drybulb Temperature Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Wind Speed Reporting FrequencyHourly Output:Variable data\n", + "Added - Site Outdoor Air Relative Humidity Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE2 Reporting FrequencyHourly Output:Variable data\n", + "Added - AHST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - ACST_Sch_BLOCK1_ZONE1 Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Cooling Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - VRF Heat Pump Heating Electricity Energy Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil Reporting FrequencyHourly Output:Variable data\n", + "Added - BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil Reporting Frequency Hourly Output:Variable data\n", + "Added - OutputControl:Files object\n", + "Added - Output:VariableDictionary object\n", + "\n", + "=======================END OF OUTPUT IDF FILE GENERATION PROCESS=======================\n", + "\n" + ] + } + ], + "source": [ + "parametric = OptimParamSimulation(\n", + " building=building,\n", + " parameters_type='accim predefined model',\n", + " #output_type='standard', #\n", + " #output_keep_existing=False, #\n", + " #output_freqs=['hourly'], #\n", + " #ScriptType='vrf_mm', #\n", + " #SupplyAirTempInputMethod='temperature difference', #\n", + " #debugging=True, #\n", + " #verbosemode=False #\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51eed0eb-7b3a-4428-8952-070a650acf61", + "metadata": {}, + "source": [ + "An initial and generic version of the Adaptive-Comfort-Control-Implementation Script (ACCIS) has been added to the idf instance `building`. For instance, you can take a look at the parameter values accis currently has:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad387b77-4998-4e51-979a-928fa52e551d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[\n", + " ENERGYMANAGEMENTSYSTEM:PROGRAM,\n", + " SetInputData, !- Name\n", + " set ComfStand = 1, !- Program Line 1\n", + " set CAT = 1, !- Program Line 2\n", + " set ComfMod = 2, !- Program Line 3\n", + " set HVACmode = 2, !- Program Line 4\n", + " set VentCtrl = 0, !- Program Line 5\n", + " set VSToffset = 0, !- Program Line 6\n", + " set MinOToffset = 7, !- Program Line 7\n", + " set MaxWindSpeed = 6, !- Program Line 8\n", + " set ACSTtol = -0.25, !- Program Line 9\n", + " set AHSTtol = 0.25, !- Program Line 10\n", + " set CoolSeasonStart = 121, !- Program Line 11\n", + " set CoolSeasonEnd = 274; !- Program Line 12]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata']" + ] + }, + { + "cell_type": "markdown", + "id": "99605e82-4d01-4aee-835c-ab9c6c66faa4", + "metadata": {}, + "source": [ + "## Setting the outputs" + ] + }, + { + "cell_type": "markdown", + "id": "bc714539-79c3-46e9-9caa-44a21fc413cb", + "metadata": {}, + "source": [ + "### Outputs for the idf (i.e. the outputs for each simulation run)" + ] + }, + { + "cell_type": "markdown", + "id": "764b7626-0e63-4317-8bbf-28ac2fe68f68", + "metadata": {}, + "source": [ + "First of all, we are going to set the outputs of the simulations that are going to be performed. This is an important step, especially if you are going to run hundreds or thousands of simulations." + ] + }, + { + "cell_type": "markdown", + "id": "b2f1d34a-dc0a-4aac-b01f-a7425b95090f", + "metadata": {}, + "source": [ + "Let's take a look at the Output:Variable objects we currently have in the idf. The method `get_output_var_df_from_idf()` returns a pandas DataFrame which contains the information of the existing Output:Variable objects in the idf:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "12ee78ba-c60b-4ef1-a6fb-51086a8a53fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
0*Comfort TemperatureHourly
1*Adaptive Cooling Setpoint TemperatureHourly
2*Adaptive Heating Setpoint TemperatureHourly
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
...............
72*VRF Heat Pump Heating Electricity EnergyHourly
73BLOCK1_ZONE2 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
74BLOCK1_ZONE2 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
75BLOCK1_ZONE1 VRF Indoor Unit DX Cooling CoilCooling Coil Total Cooling RateHourly
76BLOCK1_ZONE1 VRF Indoor Unit DX Heating CoilHeating Coil Heating RateHourly
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77 rows × 4 columns

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" + ], + "text/plain": [ + " key_value \\\n", + "0 * \n", + "1 * \n", + "2 * \n", + "3 * \n", + "4 * \n", + ".. ... \n", + "72 * \n", + "73 BLOCK1_ZONE2 VRF Indoor Unit DX Cooling Coil \n", + "74 BLOCK1_ZONE2 VRF Indoor Unit DX Heating Coil \n", + "75 BLOCK1_ZONE1 VRF Indoor Unit DX Cooling Coil \n", + "76 BLOCK1_ZONE1 VRF Indoor Unit DX Heating Coil \n", + "\n", + " variable_name reporting_frequency \\\n", + "0 Comfort Temperature Hourly \n", + "1 Adaptive Cooling Setpoint Temperature Hourly \n", + "2 Adaptive Heating Setpoint Temperature Hourly \n", + "3 Adaptive Cooling Setpoint Temperature_No Toler... Hourly \n", + "4 Adaptive Heating Setpoint Temperature_No Toler... Hourly \n", + ".. ... ... \n", + "72 VRF Heat Pump Heating Electricity Energy Hourly \n", + "73 Cooling Coil Total Cooling Rate Hourly \n", + "74 Heating Coil Heating Rate Hourly \n", + "75 Cooling Coil Total Cooling Rate Hourly \n", + "76 Heating Coil Heating Rate Hourly \n", + "\n", + " schedule_name \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + ".. ... \n", + "72 \n", + "73 \n", + "74 \n", + "75 \n", + "76 \n", + "\n", + "[77 rows x 4 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = parametric.get_output_var_df_from_idf()\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "ab1e81e8-c2f0-40c0-b868-0cf7ddb9954f", + "metadata": {}, + "source": [ + "now, let's see the Output:Meter objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "94c9844b-aacf-41fa-b1d4-ac7b491aa14e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [key_name, frequency]\n", + "Index: []" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "093bdcab-a1aa-492a-8864-d2e4e1a0b72c", + "metadata": {}, + "source": [ + "In this case, we can see there is no Output:Meter. However, there is a large number of Output:Variable objects which might result in heavy simulation outputs. So, let's get rid of some of them. We can drop the rows we want, and then input the modified DataFrame in the method `set_output_var_df_to_idf(outputs_df)`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9ccc7b08-119c-4f4c-bb45-9aac8201fe7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namereporting_frequencyschedule_name
3*Adaptive Cooling Setpoint Temperature_No Toler...Hourly
4*Adaptive Heating Setpoint Temperature_No Toler...Hourly
52*Zone Operative TemperatureHourly
54*Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "3 * Adaptive Cooling Setpoint Temperature_No Toler... \n", + "4 * Adaptive Heating Setpoint Temperature_No Toler... \n", + "52 * Zone Operative Temperature \n", + "54 * Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " reporting_frequency schedule_name \n", + "3 Hourly \n", + "4 Hourly \n", + "52 Hourly \n", + "54 Hourly " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_idf = df_output_variables_idf[\n", + " (\n", + " df_output_variables_idf['variable_name'].str.contains('Setpoint Temperature_No Tolerance')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Operative Temperature')\n", + " |\n", + " df_output_variables_idf['variable_name'].str.contains('Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')\n", + " )\n", + "]\n", + "df_output_variables_idf" + ] + }, + { + "cell_type": "markdown", + "id": "643151b4-a20d-4d00-a1c9-5a78a56be916", + "metadata": {}, + "source": [ + "Let's keep only the Output:Variable objects we have filtered using the `set_output_var_df_to_idf(outputs_df)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8958bc66-f42d-4fc0-9622-f2b3ffb03852", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf)" + ] + }, + { + "cell_type": "markdown", + "id": "eacd9d6d-7fa1-4e6d-8bab-edbfa322140b", + "metadata": {}, + "source": [ + "We have removed all rows except the adaptive heating and cooling setpoints, the operative temperature and the running mean outdoor temperature. Next optional step is adding Output:Meter objects. We can do that using the method `set_output_met_objects_to_idf(output_meters)`, where `output_meters` is a list of Output:Meter key names." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "addf36d3-b7c2-4254-bec5-322850a464d1", + "metadata": {}, + "outputs": [], + "source": [ + "output_meters = [\n", + " 'Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + "]\n", + "parametric.set_output_met_objects_to_idf(output_meters=output_meters)" + ] + }, + { + "cell_type": "markdown", + "id": "fa66a111-e72e-4ebd-8110-8222191d6c5d", + "metadata": {}, + "source": [ + "Let's see Output:Meter objects we currently have after adding these:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "41878d11-6dcc-4158-9ec4-a74aa9d2dafc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
0Heating:Electricityhourly
1Cooling:Electricityhourly
2Electricity:HVAChourly
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" + ], + "text/plain": [ + " key_name frequency\n", + "0 Heating:Electricity hourly\n", + "1 Cooling:Electricity hourly\n", + "2 Electricity:HVAC hourly" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_meters_idf = parametric.get_output_meter_df_from_idf()\n", + "df_output_meters_idf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "90fd8d9b-ecd8-4c79-9f37-775f2c2921ed", + "metadata": {}, + "source": [ + "### Outputs to be read and shown in the parametric simulation or optimisation" + ] + }, + { + "cell_type": "markdown", + "id": "bb18fd43-e07e-42f1-ac75-020bcbe96d9a", + "metadata": {}, + "source": [ + "To successfully run the parametric simulation or optimisation, it is advisable running a test simulation to know the outputs that each simulation will have. We can do that with the method `get_outputs_df_from_testsim()`, which returns a tuple containing 2 DataFrames containing respectively the Output:Meter and Output:Variable objects from the simulation. In this case, you won't find wildcards such as \"*\"." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ff6093e9-840d-4142-ad79-98d78f4f13d1", + "metadata": {}, + "outputs": [], + "source": [ + "df_output_meters_testsim, df_output_variables_testsim = parametric.get_outputs_df_from_testsim()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "61a88134-7997-42ce-88b0-ef4606cdfe61", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_namefrequency
0Heating:ElectricityHourly
1Cooling:ElectricityHourly
2Electricity:HVACHourly
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key_valuevariable_namefrequency
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly
2BLOCK1:ZONE2Zone Operative TemperatureHourly
3BLOCK1:ZONE1Zone Operative TemperatureHourly
4PEOPLE BLOCK1:ZONE2Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "2 BLOCK1:ZONE2 Zone Operative Temperature \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "4 PEOPLE BLOCK1:ZONE2 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency \n", + "0 Hourly \n", + "1 Hourly \n", + "2 Hourly \n", + "3 Hourly \n", + "4 Hourly \n", + "5 Hourly " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_output_variables_testsim" + ] + }, + { + "cell_type": "markdown", + "id": "b0ed4500-6054-4a00-ae63-54412bf70944", + "metadata": {}, + "source": [ + "We can get DataFrames from the .rdd and .mdd files generated from the test simulation using the functions `get_rdd_file_as_df()` and `get_mdd_file_as_df()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e28454a5-8de7-4551-bf9b-4db3fa8f124f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectkey_valuevariable_namefrequencyunits
0Output:Variable*Site Outdoor Air Drybulb Temperaturehourly!- Zone Average [C]
1Output:Variable*Site Outdoor Air Dewpoint Temperaturehourly!- Zone Average [C]
2Output:Variable*Site Outdoor Air Wetbulb Temperaturehourly!- Zone Average [C]
3Output:Variable*Site Outdoor Air Humidity Ratiohourly!- Zone Average [kgWater/kgDryAir]
4Output:Variable*Site Outdoor Air Relative Humidityhourly!- Zone Average [%]
..................
712Output:Variable*Zone Ventilation When Unoccupied Timehourly!- HVAC Sum [hr]
713Output:Variable*Facility Any Zone Ventilation Below Target Voz...hourly!- HVAC Sum [hr]
714Output:Variable*Facility All Zones Ventilation At Target Voz Timehourly!- HVAC Sum [hr]
715Output:Variable*Facility Any Zone Ventilation Above Target Voz...hourly!- HVAC Sum [hr]
716Output:Variable*Facility Any Zone Ventilation When Unoccupied ...hourly!- HVAC Sum [hr]
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717 rows × 5 columns

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" + ], + "text/plain": [ + " object key_value \\\n", + "0 Output:Variable * \n", + "1 Output:Variable * \n", + "2 Output:Variable * \n", + "3 Output:Variable * \n", + "4 Output:Variable * \n", + ".. ... ... \n", + "712 Output:Variable * \n", + "713 Output:Variable * \n", + "714 Output:Variable * \n", + "715 Output:Variable * \n", + "716 Output:Variable * \n", + "\n", + " variable_name frequency \\\n", + "0 Site Outdoor Air Drybulb Temperature hourly \n", + "1 Site Outdoor Air Dewpoint Temperature hourly \n", + "2 Site Outdoor Air Wetbulb Temperature hourly \n", + "3 Site Outdoor Air Humidity Ratio hourly \n", + "4 Site Outdoor Air Relative Humidity hourly \n", + ".. ... ... \n", + "712 Zone Ventilation When Unoccupied Time hourly \n", + "713 Facility Any Zone Ventilation Below Target Voz... hourly \n", + "714 Facility All Zones Ventilation At Target Voz Time hourly \n", + "715 Facility Any Zone Ventilation Above Target Voz... hourly \n", + "716 Facility Any Zone Ventilation When Unoccupied ... hourly \n", + "\n", + " units \n", + "0 !- Zone Average [C] \n", + "1 !- Zone Average [C] \n", + "2 !- Zone Average [C] \n", + "3 !- Zone Average [kgWater/kgDryAir] \n", + "4 !- Zone Average [%] \n", + ".. ... \n", + "712 !- HVAC Sum [hr] \n", + "713 !- HVAC Sum [hr] \n", + "714 !- HVAC Sum [hr] \n", + "715 !- HVAC Sum [hr] \n", + "716 !- HVAC Sum [hr] \n", + "\n", + "[717 rows x 5 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rdd = get_rdd_file_as_df()\n", + "df_rdd" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "58649f6e-f08c-49e5-823a-376ff26eb83a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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objectmeter_namefrequencyunits
0Output:MeterElectricity:Facilityhourly!- [J]
1Output:Meter:CumulativeElectricity:Facilityhourly!- [J]
2Output:MeterElectricity:Buildinghourly!- [J]
3Output:Meter:CumulativeElectricity:Buildinghourly!- [J]
4Output:MeterElectricity:Zone:BLOCK1:ZONE2hourly!- [J]
...............
157Output:Meter:CumulativeGeneral:HeatRecovery:EnergyTransferhourly!- [J]
158Output:MeterCarbon Equivalent:Facilityhourly!- [kg]
159Output:Meter:CumulativeCarbon Equivalent:Facilityhourly!- [kg]
160Output:MeterCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
161Output:Meter:CumulativeCarbonEquivalentEmissions:Carbon Equivalenthourly!- [kg]
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162 rows × 4 columns

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" + ], + "text/plain": [ + " object meter_name \\\n", + "0 Output:Meter Electricity:Facility \n", + "1 Output:Meter:Cumulative Electricity:Facility \n", + "2 Output:Meter Electricity:Building \n", + "3 Output:Meter:Cumulative Electricity:Building \n", + "4 Output:Meter Electricity:Zone:BLOCK1:ZONE2 \n", + ".. ... ... \n", + "157 Output:Meter:Cumulative General:HeatRecovery:EnergyTransfer \n", + "158 Output:Meter Carbon Equivalent:Facility \n", + "159 Output:Meter:Cumulative Carbon Equivalent:Facility \n", + "160 Output:Meter CarbonEquivalentEmissions:Carbon Equivalent \n", + "161 Output:Meter:Cumulative CarbonEquivalentEmissions:Carbon Equivalent \n", + "\n", + " frequency units \n", + "0 hourly !- [J] \n", + "1 hourly !- [J] \n", + "2 hourly !- [J] \n", + "3 hourly !- [J] \n", + "4 hourly !- [J] \n", + ".. ... ... \n", + "157 hourly !- [J] \n", + "158 hourly !- [kg] \n", + "159 hourly !- [kg] \n", + "160 hourly !- [kg] \n", + "161 hourly !- [kg] \n", + "\n", + "[162 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mdd = get_mdd_file_as_df()\n", + "df_mdd" + ] + }, + { + "cell_type": "markdown", + "id": "a84943b5-db09-4f19-95bb-f9583711d0a7", + "metadata": {}, + "source": [ + "Also, we can parse the .mtd files as a list using the function `parse_mtd_file()`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df4da1a7-fcb2-4c59-b790-1bc9e3b128f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'meter_id': '14',\n", + " 'description': 'BLOCK1:ZONE2 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE2 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone2#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']},\n", + " {'meter_id': '135',\n", + " 'description': 'BLOCK1:ZONE1 GENERAL LIGHTING:Lights Electricity Energy [J]',\n", + " 'on_meters': ['Electricity:Facility [J]',\n", + " 'Electricity:Building [J]',\n", + " 'Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'Electricity:SpaceType:GENERAL [J]',\n", + " 'InteriorLights:Electricity [J]',\n", + " 'InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'InteriorLights:Electricity:SpaceType:GENERAL [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:Zone:BLOCK1:ZONE1 [J]',\n", + " 'ELECTRIC EQUIPMENT#Block1:Zone1#GeneralLights:InteriorLights:Electricity:SpaceType:GENERAL [J]']}]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mtd_list = parse_mtd_file()\n", + "mtd_list[0:2]" + ] + }, + { + "cell_type": "markdown", + "id": "76fa398d-15a0-4504-9f11-468bfd7c094f", + "metadata": {}, + "source": [ + "Therefore, we have 2 DataFrames, one for the Output:Meter and another for the Output:Variable objects. Next step is setting the outputs for the parametric simulation. To do so, we'll need to pass the DataFrames into the method `set_outputs_for_simulation(df_output_meter, df_output_variable)`. If you have some knowledge about the python package besos, you might think of these dataframes as if each row was a `MeterReader` or `VariableReader` instances respectively for the Output:Meter and Output:Variable dataframes, and the arguments in these were the specified in the columns. The `MeterReader` class takes the arguments `key_name`, `frequency`, `name` and `func`, while `VariableReader` class takes the arguments `key_value`, `variable_name`, `frequency`, `name` and `func`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b133883-a0be-42a9-bc04-0e6054337b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_name', 'frequency']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_meters_testsim.columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c43b0496-1d36-4a0e-b2dc-9f77e41627ff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['key_value', 'variable_name', 'frequency']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i for i in df_output_variables_testsim.columns]" + ] + }, + { + "cell_type": "markdown", + "id": "a03bd526-8bdf-4cce-af62-73ac36e9d0c3", + "metadata": {}, + "source": [ + "If you take a look at the columns of the dataframes above, you can see the names are the arguments in the `MeterReader` and `VariableReader` classes, and only `name` and `func` are missing. That means, you can add these columns to input the `name` and `func` arguments as desired. In case of the Output:Meter dataframe, we won't add the `name` and `func` columns, which means the name will be the `key_name` and hourly results will be aggregated using the pd.Series.sum() function. However, in case of the Output:Variable dataframe, we will specify these: we want the hourly values rather than the aggregation, therefore we will pass the name bound to the function `return_time_series`, and we will add '_time series' as a suffix to the `variable_name` column. We will also remove the outputs for BLOCK1:ZONE2, which are the rows 2 and 4." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0e8fecdc-4625-4d95-a87d-67071884963b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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key_valuevariable_namefrequencyfuncname
0EMSAdaptive Cooling Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x00000267CB9B...Adaptive Cooling Setpoint Temperature_No Toler...
1EMSAdaptive Heating Setpoint Temperature_No Toler...Hourly<function return_time_series at 0x00000267CB9B...Adaptive Heating Setpoint Temperature_No Toler...
3BLOCK1:ZONE1Zone Operative TemperatureHourly<function return_time_series at 0x00000267CB9B...Zone Operative Temperature_time series
5PEOPLE BLOCK1:ZONE1Zone Thermal Comfort ASHRAE 55 Adaptive Model ...Hourly<function return_time_series at 0x00000267CB9B...Zone Thermal Comfort ASHRAE 55 Adaptive Model ...
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" + ], + "text/plain": [ + " key_value variable_name \\\n", + "0 EMS Adaptive Cooling Setpoint Temperature_No Toler... \n", + "1 EMS Adaptive Heating Setpoint Temperature_No Toler... \n", + "3 BLOCK1:ZONE1 Zone Operative Temperature \n", + "5 PEOPLE BLOCK1:ZONE1 Zone Thermal Comfort ASHRAE 55 Adaptive Model ... \n", + "\n", + " frequency func \\\n", + "0 Hourly , key_name='Heating:Electricity'),\n", + " MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'),\n", + " MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'),\n", + " VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'),\n", + " VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'),\n", + " VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.sim_outputs" + ] + }, + { + "cell_type": "markdown", + "id": "b5dd98bb-6b1d-4b3c-b762-3c9a0c015f63", + "metadata": {}, + "source": [ + "## Setting the parameters" + ] + }, + { + "cell_type": "markdown", + "id": "4804beed-52e5-432a-a999-685c222d668d", + "metadata": {}, + "source": [ + "At the top of the script, when you instantiated the class `OptimParamSimulation`, you already specified which type of parameters you were going to use. Now, the parameters we're about to set, must match the `parameters_type` argument. At this point, you may not know which parameters you can use, so you can call the method `get_available_parameters()`, which will return a list of available parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "004029fa-ff72-4f43-a9c3-e004757463e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand',\n", + " 'CAT',\n", + " 'CATcoolOffset',\n", + " 'CATheatOffset',\n", + " 'ComfMod',\n", + " 'SetpointAcc',\n", + " 'CoolSeasonStart',\n", + " 'CoolSeasonEnd',\n", + " 'HVACmode',\n", + " 'VentCtrl',\n", + " 'MaxTempDiffVOF',\n", + " 'MinTempDiffVOF',\n", + " 'MultiplierVOF',\n", + " 'VSToffset',\n", + " 'MinOToffset',\n", + " 'MaxWindSpeed',\n", + " 'ASTtol']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "available_parameters = parametric.get_available_parameters()\n", + "available_parameters" + ] + }, + { + "cell_type": "markdown", + "id": "45886126-30b8-4e53-b8c0-06135e6a3c38", + "metadata": {}, + "source": [ + "If you don't know what are these, please refer to the [documentation](https://accim.readthedocs.io/en/master/4_detailed%20use.html).\n", + "\n", + "Using the 'accim predefined model' type, the values must be a list of options, since the values for some arguments are used to select some specific comfort model, and have a categorical use rather than numeric. Now, let's set the parameters using the method `set_parameters(accis_params_dict, additional_params)`. In this method, we set the parameters related to accim using the argument `accis_params_dict`, which takes a dictionary following the pattern {'parameter name': [1, 2, 3, etc]}. We can also add some other parameters not related to accim in the argument `additional_params`, which takes a list of parameters as if these were input straight to the besos EPProblem class. If additional arguments are added, the descriptors must be `CategoryParameter`." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b4eb43dd-879b-4175-ab56-e9c04ea342af", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named ComfStand.The name used as an input (ComfStand) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named CAT.The name used as an input (CAT) will be discarded.\n", + " warnings.warn(\n", + "C:\\Users\\usuario\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\besos\\parameters.py:441: UserWarning: This parameter's descriptor is already named ComfMod.The name used as an input (ComfMod) will be discarded.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "accis_parameters = {\n", + " 'ComfStand': [1, 2],\n", + " 'CAT': [80, 90],\n", + " 'ComfMod': [3],\n", + "}\n", + "parametric.set_parameters(accis_params_dict=accis_parameters)" + ] + }, + { + "cell_type": "markdown", + "id": "b2e1e8a5-4af9-4d23-82b9-af45b49ba26d", + "metadata": {}, + "source": [ + "If you want to inspect the `Parameter` objects, you can see the internal variable `parameters_list`:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4b72a5b5-417f-410b-b30a-bf6550dbb364", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]),\n", + " Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_list" + ] + }, + { + "cell_type": "markdown", + "id": "16b3b170-683d-49b3-9d6b-4d3772245647", + "metadata": {}, + "source": [ + "## Running the parametric simulation" + ] + }, + { + "cell_type": "markdown", + "id": "e36956e3-781b-4fe1-980f-ad9fbca21fba", + "metadata": {}, + "source": [ + "### Setting the problem" + ] + }, + { + "cell_type": "markdown", + "id": "60150ec0-8c63-4097-bd4f-c8263dc22d47", + "metadata": {}, + "source": [ + "First, let's set the problem. To do so, use the `set_problem()` method. In case of the parametric simulation you don't need to input any argument. However, in case of the optimisation, you must input the arguments `minimize_outputs`, `constraints` and `constraint_bounds`, similarly as you would do in the besos `EPProblem` class." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "cd2c3ebc-99ea-46a2-94cf-37b3ddd77630", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.set_problem()" + ] + }, + { + "cell_type": "markdown", + "id": "cc1eac32-9784-441f-98e3-0382053ba14d", + "metadata": {}, + "source": [ + "Again, you can inspect the `EPProblem` class instance in the internal variable `problem`:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a4f80f41-febc-47e0-b610-8c07893a5c20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EPProblem(inputs=[Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfStand', options=[1, 2])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='CAT', options=[80, 90])]), Parameter(selector=GenericSelector(set=), value_descriptors=[CategoryParameter(name='ComfMod', options=[3])])], outputs=[MeterReader(name='Heating:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Heating:Electricity'), MeterReader(name='Cooling:Electricity', class_name='Output:Meter', frequency='Hourly', func=, key_name='Cooling:Electricity'), MeterReader(name='Electricity:HVAC', class_name='Output:Meter', frequency='Hourly', func=, key_name='Electricity:HVAC'), VariableReader(name='Adaptive Cooling Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Cooling Setpoint Temperature_No Tolerance'), VariableReader(name='Adaptive Heating Setpoint Temperature_No Tolerance_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='EMS', variable_name='Adaptive Heating Setpoint Temperature_No Tolerance'), VariableReader(name='Zone Operative Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='BLOCK1:ZONE1', variable_name='Zone Operative Temperature'), VariableReader(name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series', class_name='Output:Variable', frequency='Hourly', func=, key_value='PEOPLE BLOCK1:ZONE1', variable_name='Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature')], minimize_outputs=[True, True, True, True, True, True, True], converters={'outputs': , 'constraints': })" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.problem" + ] + }, + { + "cell_type": "markdown", + "id": "901b319a-2ef8-4c7c-9fea-7512b81807f0", + "metadata": {}, + "source": [ + "### Sampling the simulation runs" + ] + }, + { + "cell_type": "markdown", + "id": "cecbc846-7227-498a-b0f8-f8795269df25", + "metadata": {}, + "source": [ + "The way to inform besos of the variations and permutations it must carry out in the parametric analysis is by means of a DataFrame, which must contain a column per `Parameter`, in which values are specified. There are multiple ways to do this DataFrame. For instance, we could make a dataframe from scratch:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "30f78385-766d-47f2-b394-c922f8403b30", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ComfStand CAT ComfMod\n", + "0 2 80 3\n", + "1 2 90 3" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "param_dict = {'ComfStand': [2, 2], 'CAT': [80, 90], 'ComfMod': [3, 3]}\n", + "input_param_df = pd.DataFrame(data=param_dict)\n", + "input_param_df" + ] + }, + { + "cell_type": "markdown", + "id": "9a525ce0-8b59-48ca-a160-59ff4781a8bb", + "metadata": {}, + "source": [ + "Also, we can use the sampling functions from besos (`full_factorial` and `lhs`), although these are not available using the accim predefined models, since these are based on ranges of values instead of options. In this case, we could use the `sampling_full_set()` method, which will combine all the values we entered when we set the parameters and drop the invalid combinations (e.g. you cannot use CAT=1, 2 or 3 if you are using ComfStand=2, since these CAT values are only available for ComfStand=1)." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "286da8cc-4ee2-41b3-9d3a-07ba98f708e0", + "metadata": {}, + "outputs": [], + "source": [ + "parametric.sampling_full_set()" + ] + }, + { + "cell_type": "markdown", + "id": "4f55e72f-f2b7-4f8a-940e-af5d23e0aa72", + "metadata": {}, + "source": [ + "Now, you can see the resulting input parameter dataframe in the internal variable `parameters_values_df`:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "dfebdd41-ee54-4fcf-ab7e-12db7e9461fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ComfStand CAT ComfMod\n", + "2 2 80 3\n", + "3 2 90 3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.parameters_values_df" + ] + }, + { + "cell_type": "markdown", + "id": "c0c976c6-eed1-4727-a274-975585466130", + "metadata": {}, + "source": [ + "### Running the simulations" + ] + }, + { + "cell_type": "markdown", + "id": "302f66b3-55ba-468b-91bb-d0c7a53f4387", + "metadata": {}, + "source": [ + "Now, we're ready to run the simulations, by means of the `run_parametric_simulation(epws, out_dir, df, processes)` method. After calling the method, the outputs (a DataFrame) is saved in the internal variable `outputs_param_simulation`. It is based on the use of the `EvaluatorEP` class, `df_apply` method. We want to run the parametric simulations with both Sydney and Seville climate files, therefore the filenames are input in a list in the `epws` argument. The simulation outputs will be saved in a directory named 'notebook_temp_dir'. The values for the parameters will be driven by the internal variable `parameters_values_df`, as input in the `df` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5e5a19ab-d2a3-44ac-9d2d-01672ac8db08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n", + "Running EnergyPlus with stdout output suppressed...\n" + ] + } + ], + "source": [ + "parametric.run_parametric_simulation(\n", + " epws=['Sydney.epw', 'Seville.epw'],\n", + " out_dir='notebook_temp_dir',\n", + " df=parametric.parameters_values_df,\n", + " #processes=6, # The number of CPUs to be used. Default is 2.\n", + " #keep_input=True, # To keep the input values of parameters, as entered in df argument. Default is True.\n", + " #keep_dirs=True # To keep the simulation outputs. Default is True.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "8f768acb-e813-4d4d-91d7-7b96a98ecaad", + "metadata": {}, + "source": [ + "Let's take a look at the simulation results" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9461f205-acd1-4532-b691-9dd7d9778873", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexComfStandCATComfModHeating:ElectricityCooling:ElectricityElectricity:HVACAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time seriesoutput_direpw
0228032.593138e+091.754898e+102.735351e+10[27.325804398148147, 27.91096527777778, 27.910...[20.325804398148147, 20.910965277777777, 20.91...[23.771739074031462, 23.897364637263966, 23.75...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8...Sydney
1329033.639536e+093.621666e+104.728630e+10[26.325804398148147, 26.91096527777778, 26.910...[21.325804398148147, 21.910965277777777, 21.91...[23.66668667903001, 23.8347876559133, 23.82938...[21.325694444444437, 21.325694444444437, 21.32...notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH...Sydney
2228032.062257e+093.171532e+104.054615e+10[24.785096064814812, 24.86211527777778, 24.862...[17.785096064814812, 17.86211527777778, 17.862...[21.151988618198942, 21.208271993109328, 20.26...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR...Seville
3329032.297988e+094.516874e+105.445162e+10[23.785096064814812, 23.86211527777778, 23.862...[18.785096064814812, 18.86211527777778, 18.862...[22.631374958463137, 20.71719912972282, 20.492...[11.490694444444445, 11.490694444444445, 11.49...notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H...Seville
\n", + "
" + ], + "text/plain": [ + " index ComfStand CAT ComfMod Heating:Electricity Cooling:Electricity \\\n", + "0 2 2 80 3 2.593138e+09 1.754898e+10 \n", + "1 3 2 90 3 3.639536e+09 3.621666e+10 \n", + "2 2 2 80 3 2.062257e+09 3.171532e+10 \n", + "3 3 2 90 3 2.297988e+09 4.516874e+10 \n", + "\n", + " Electricity:HVAC \\\n", + "0 2.735351e+10 \n", + "1 4.728630e+10 \n", + "2 4.054615e+10 \n", + "3 5.445162e+10 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 [27.325804398148147, 27.91096527777778, 27.910... \n", + "1 [26.325804398148147, 26.91096527777778, 26.910... \n", + "2 [24.785096064814812, 24.86211527777778, 24.862... \n", + "3 [23.785096064814812, 23.86211527777778, 23.862... \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 [20.325804398148147, 20.910965277777777, 20.91... \n", + "1 [21.325804398148147, 21.910965277777777, 21.91... \n", + "2 [17.785096064814812, 17.86211527777778, 17.862... \n", + "3 [18.785096064814812, 18.86211527777778, 18.862... \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 [23.771739074031462, 23.897364637263966, 23.75... \n", + "1 [23.66668667903001, 23.8347876559133, 23.82938... \n", + "2 [21.151988618198942, 21.208271993109328, 20.26... \n", + "3 [22.631374958463137, 20.71719912972282, 20.492... \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \\\n", + "0 [21.325694444444437, 21.325694444444437, 21.32... \n", + "1 [21.325694444444437, 21.325694444444437, 21.32... \n", + "2 [11.490694444444445, 11.490694444444445, 11.49... \n", + "3 [11.490694444444445, 11.490694444444445, 11.49... \n", + "\n", + " output_dir epw \n", + "0 notebook_temp_dir\\BESOS_Output\\3XGJQRLTICGY3K8... Sydney \n", + "1 notebook_temp_dir\\BESOS_Output\\KYO6E0BHDFYY3PH... Sydney \n", + "2 notebook_temp_dir\\BESOS_Output\\T8OPMW5P338ZFRR... Seville \n", + "3 notebook_temp_dir\\BESOS_Output\\43Q9COP1KO2RD5H... Seville " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.outputs_param_simulation" + ] + }, + { + "cell_type": "markdown", + "id": "efa37b3d-45f9-4bbd-b2c2-3f36b743ee0b", + "metadata": {}, + "source": [ + "We can see the columns are the following:" + ] + }, + { + "cell_type": "markdown", + "id": "907ca41e-e151-4e90-a37b-ed0b596e1852", + "metadata": {}, + "source": [ + "- the parameters, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c733f7a2-fd24-4b14-9ab1-98075d261401", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ComfStand', 'CAT', 'ComfMod']" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.value_descriptors[0].name for i in parametric.parameters_list]" + ] + }, + { + "cell_type": "markdown", + "id": "c30f391f-08a0-488e-8dd3-67aa9189790c", + "metadata": {}, + "source": [ + "- the outputs, which are:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "405d7408-599d-4493-a497-5bfe686db381", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Heating:Electricity',\n", + " 'Cooling:Electricity',\n", + " 'Electricity:HVAC',\n", + " 'Adaptive Cooling Setpoint Temperature_No Tolerance_time series',\n", + " 'Adaptive Heating Setpoint Temperature_No Tolerance_time series',\n", + " 'Zone Operative Temperature_time series',\n", + " 'Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[i.name for i in parametric.sim_outputs]" + ] + }, + { + "cell_type": "markdown", + "id": "cf172d66-6883-481c-9779-a3382616924e", + "metadata": {}, + "source": [ + "- the path to the output files for each simulation, in the column 'out_dir'\n", + "- the epw for each simulation, in the column 'epw'" + ] + }, + { + "cell_type": "markdown", + "id": "e2f6302e-8105-482b-a984-c4bc03ac8086", + "metadata": {}, + "source": [ + "### Visualising the results" + ] + }, + { + "cell_type": "markdown", + "id": "8de1cbd1-8cf0-41c4-b394-6cf8481f2edd", + "metadata": {}, + "source": [ + "#### Aggregated columns" + ] + }, + { + "cell_type": "markdown", + "id": "d7b2ad11-4f2b-46d0-8a59-3e7e842b2ae9", + "metadata": {}, + "source": [ + "At this point, if you have some knowledge of pandas and some package to plot the data (e.g. matplotlib or seaborn), you can carry out your own analysis and visualization. We're going to do some example below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3b08c431-5c10-47bc-b8d9-bbed174fb35c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(\n", + " data=parametric.outputs_param_simulation,\n", + " x='Heating:Electricity',\n", + " y='Cooling:Electricity',\n", + " hue='CAT',\n", + " style='epw'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "710213aa-c8d2-4565-b121-a0db80753b52", + "metadata": {}, + "source": [ + "#### Time series columns" + ] + }, + { + "cell_type": "markdown", + "id": "d66994c0-7514-456f-8d9c-2c3acce6e656", + "metadata": {}, + "source": [ + "If you requested some output to be reported in time series, you can get a dataframe containing the hourly values using the method `get_hourly_df()`. This dataframe is saved in the internal variable `outputs_param_simulation_hourly`:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "74848927-55f4-4e27-ae75-bf66baf49ba7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ComfStandCATComfModepwhourdatetimeAdaptive Cooling Setpoint Temperature_No Tolerance_time seriesAdaptive Heating Setpoint Temperature_No Tolerance_time seriesZone Operative Temperature_time seriesZone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series
02803Sydney12024-01-01 01:00:0027.32580420.32580423.77173921.325694
12803Sydney22024-01-01 02:00:0027.91096520.91096523.89736521.325694
22803Sydney32024-01-01 03:00:0027.91096520.91096523.75949921.325694
32803Sydney42024-01-01 04:00:0027.91096520.91096523.81012121.325694
42803Sydney52024-01-01 05:00:0027.91096520.91096524.11513121.325694
.................................
350352903Seville87562024-12-30 20:00:0023.93343018.93343023.61795711.720741
350362903Seville87572024-12-30 21:00:0023.93343018.93343023.57101211.720741
350372903Seville87582024-12-30 22:00:0023.93343018.93343023.34048011.720741
350382903Seville87592024-12-30 23:00:0023.93343018.93343023.06050111.720741
350392903Seville87602024-12-31 00:00:0023.93343018.93343022.83835211.720741
\n", + "

35040 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " ComfStand CAT ComfMod epw hour datetime \\\n", + "0 2 80 3 Sydney 1 2024-01-01 01:00:00 \n", + "1 2 80 3 Sydney 2 2024-01-01 02:00:00 \n", + "2 2 80 3 Sydney 3 2024-01-01 03:00:00 \n", + "3 2 80 3 Sydney 4 2024-01-01 04:00:00 \n", + "4 2 80 3 Sydney 5 2024-01-01 05:00:00 \n", + "... ... ... ... ... ... ... \n", + "35035 2 90 3 Seville 8756 2024-12-30 20:00:00 \n", + "35036 2 90 3 Seville 8757 2024-12-30 21:00:00 \n", + "35037 2 90 3 Seville 8758 2024-12-30 22:00:00 \n", + "35038 2 90 3 Seville 8759 2024-12-30 23:00:00 \n", + "35039 2 90 3 Seville 8760 2024-12-31 00:00:00 \n", + "\n", + " Adaptive Cooling Setpoint Temperature_No Tolerance_time series \\\n", + "0 27.325804 \n", + "1 27.910965 \n", + "2 27.910965 \n", + "3 27.910965 \n", + "4 27.910965 \n", + "... ... \n", + "35035 23.933430 \n", + "35036 23.933430 \n", + "35037 23.933430 \n", + "35038 23.933430 \n", + "35039 23.933430 \n", + "\n", + " Adaptive Heating Setpoint Temperature_No Tolerance_time series \\\n", + "0 20.325804 \n", + "1 20.910965 \n", + "2 20.910965 \n", + "3 20.910965 \n", + "4 20.910965 \n", + "... ... \n", + "35035 18.933430 \n", + "35036 18.933430 \n", + "35037 18.933430 \n", + "35038 18.933430 \n", + "35039 18.933430 \n", + "\n", + " Zone Operative Temperature_time series \\\n", + "0 23.771739 \n", + "1 23.897365 \n", + "2 23.759499 \n", + "3 23.810121 \n", + "4 24.115131 \n", + "... ... \n", + "35035 23.617957 \n", + "35036 23.571012 \n", + "35037 23.340480 \n", + "35038 23.060501 \n", + "35039 22.838352 \n", + "\n", + " Zone Thermal Comfort ASHRAE 55 Adaptive Model Running Average Outdoor Air Temperature_time series \n", + "0 21.325694 \n", + "1 21.325694 \n", + "2 21.325694 \n", + "3 21.325694 \n", + "4 21.325694 \n", + "... ... \n", + "35035 11.720741 \n", + "35036 11.720741 \n", + "35037 11.720741 \n", + "35038 11.720741 \n", + "35039 11.720741 \n", + "\n", + "[35040 rows x 10 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parametric.get_hourly_df()\n", + "parametric.outputs_param_simulation_hourly" + ] + }, + { + "cell_type": "markdown", + "id": "dbac2c53-1b49-43a5-b8c7-02877ec8b4aa", + "metadata": {}, + "source": [ + "Let's prepare the `outputs_param_simulation_hourly` df for plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "921324d1-f102-4bd3-b43e-340e435bdca0", + "metadata": {}, + "outputs": [], + "source": [ + "#Let's make a copy of the dataframe to not to modify the original one\n", + "df = parametric.outputs_param_simulation_hourly.copy()\n", + "\n", + "# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot:\n", + "rmot = [i for i in df.columns if 'Running Average' in i][0]\n", + "\n", + "#Let's remove the columns where value is the same for all rows\n", + "for c in df.columns:\n", + " if len(set(df[c])) == 1:\n", + " df = df.drop(columns=[c])\n", + "#Now let's remove the hour and datetime columns, since will\n", + "df = df.drop(columns=['hour'])\n", + "\n", + "# Now let's reshape the df for plotting purposes\n", + "df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot])\n" + ] + }, + { + "cell_type": "markdown", + "id": "a4aa2a50-88ee-4c66-ab3a-48a26eaec9c1", + "metadata": {}, + "source": [ + "Firstly, we're going to plot the hourly temperatures to see the slope of the comfort model, and check all hours are within thermal comfort limits (since we applied adaptive setpoints)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2213ebd0-2f3c-4e74-bd06-117ec72c3108", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.scatterplot,\n", + " x=rmot,\n", + " y='value',\n", + " hue='variable',\n", + " s=1,\n", + " #alpha=0.5\n", + ")\n", + "g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "\n", + "for lh in g._legend.legend_handles:\n", + " lh.set_markersize(5)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "14dfbd82-2e4f-45b6-96ca-da72265e117c", + "metadata": {}, + "source": [ + "Now, we're going to plot time on x-axis and change the plot type to lineplot, to see the variarion of the indoor operative temperature throughout the year:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "b673ca88-bbd8-4c89-989e-7cf7c613e90c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(\n", + " data=df,\n", + " row='CAT',\n", + " col='epw'\n", + ")\n", + "g.map_dataframe(\n", + " sns.lineplot,\n", + " x='datetime',\n", + " y='value',\n", + " hue='variable',\n", + ")\n", + "g.set_axis_labels('Time', 'Indoor Operative Temperature (°C)')\n", + "g.add_legend(loc='upper center', bbox_to_anchor=(0.5, 0))\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e0b253ef-b8ee-402b-a741-b4b89181e589", + "metadata": {}, + "source": [ + "We're done with the example, so let's remove all new files, so that we can re-run it again." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "abefda96-72f4-4e8a-8d2a-5337647c480b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'available_outputs', 'notebook_temp_dir'}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_files = [i for i in listdir()]\n", + "new_files = set(current_files) - set(original_files)\n", + "new_files" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f9dbc462-7f2b-40e2-b7b0-41e9243b3b59", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "for item in new_files:\n", + " item_path = os.path.join(os.getcwd(), item)\n", + " if os.path.isfile(item_path):\n", + " os.remove(item_path)\n", + " elif os.path.isdir(item_path):\n", + " shutil.rmtree(item_path)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dist/accim-0.7.5.tar.gz b/dist/accim-0.7.5.tar.gz index 428aba4..a26c5cb 100644 Binary files a/dist/accim-0.7.5.tar.gz and b/dist/accim-0.7.5.tar.gz differ diff --git a/parametric_simulation_usage_v00_accim_custom.py b/parametric_simulation_usage_v00_accim_custom.py index 82e995c..911ac67 100644 --- a/parametric_simulation_usage_v00_accim_custom.py +++ b/parametric_simulation_usage_v00_accim_custom.py @@ -40,7 +40,7 @@ accim.utils.set_occupancy_to_always(idf_object=building) -test_class_instance = OptimParamSimulation( +parametric = OptimParamSimulation( building=building, parameters_type='accim custom model' # output_keep_existing=False, @@ -50,7 +50,7 @@ # Setting the Output:Variable and Output:Meter objects in the idf #todo do not print on screen the process of accis, only the first time -df_output_variables_idf = test_class_instance.get_output_var_df_from_idf() +df_output_variables_idf = parametric.get_output_var_df_from_idf() df_output_variables_idf_mod = df_output_variables_idf.copy() @@ -68,7 +68,7 @@ [i for i in building.idfobjects['energymanagementsystem:program'] if i.Name.lower() == 'setinputdata'] -test_class_instance.set_output_var_df_to_idf(outputs_df=df_output_variables_idf_mod) +parametric.set_output_var_df_to_idf(outputs_df=df_output_variables_idf_mod) output_meters = [ # 'HeatingCoils:EnergyTransfer', @@ -77,10 +77,10 @@ 'Cooling:Electricity', 'Electricity:HVAC', ] -test_class_instance.set_output_met_objects_to_idf(output_meters=output_meters) +parametric.set_output_met_objects_to_idf(output_meters=output_meters) # Checking the Output:Meter and Output:Variable objects in the simulation -df_outputmeters_2, df_outputvariables_2 = test_class_instance.get_outputs_df_from_testsim() +df_outputmeters_2, df_outputvariables_2 = parametric.get_outputs_df_from_testsim() #Other variables could be reported. These can be read in the rdd, mdd and mtd files df_rdd = get_rdd_file_as_df() @@ -105,7 +105,7 @@ def return_time_series(result): df_outputvariables_3 = df_outputvariables_3.drop(index=[2, 4]) df_outputvariables_3['name'] = df_outputvariables_3['variable_name'] + '_time series' -test_class_instance.set_outputs_for_simulation( +parametric.set_outputs_for_simulation( df_output_meter=df_outputmeters_3, # df_output_variable=df_outputvariables_3, df_output_variable=df_outputvariables_3, @@ -145,33 +145,64 @@ def return_time_series(result): # from besos.parameters import wwr, RangeParameter # other_parameters = [wwr(RangeParameter(0.1, 0.9))] -test_class_instance.set_parameters( +parametric.set_parameters( accis_params_dict=accis_parameters, # additional_params=other_parameters ) +## +param_dict = { + 'CustAST_m': [0.1, 0.6], + 'CustAST_n': [22, 8], + 'CustAST_ASToffset': [2.5, 4], + 'CustAST_ASTall': [10, 10], + 'CustAST_ASTaul': [35, 35], +} +from accim.parametric_and_optimisation.utils import make_all_combinations +all_combinations = make_all_combinations(param_dict) +## [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setinputdata'] [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setvofinputdata'] [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'applycat'] +## +set_input_data = [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setinputdata'][0] +set_vof_input_data = [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setvofinputdata'][0] +applycat = [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'applycat'][0] +setast = [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setast'][0] +setapplimits = [i for i in building.idfobjects['EnergyManagementSystem:Program'] if i.Name.lower() == 'setapplimits'][0] +other_args = {'SetpointAcc': setast.Program_Line_1} +cust_ast_args = { + 'ACSToffset': applycat.Program_Line_4, + 'AHSToffset': applycat.Program_Line_5, + 'm': setast.Program_Line_2, + 'n': setast.Program_Line_3, + 'ACSTaul': setapplimits.Program_Line_2, + 'ACSTall': setapplimits.Program_Line_3, + 'AHSTaul': setapplimits.Program_Line_4, + 'AHSTall': setapplimits.Program_Line_5, +} + +# building.savecopy('TestModel_mod.idf') +## # Let's set the problem -test_class_instance.set_problem() +parametric.set_problem() # Let's generate a sampling dataframe -test_class_instance.sampling_full_factorial(level=5) -temp_full_fac = test_class_instance.parameters_values_df +parametric.sampling_full_factorial(level=5) +temp_full_fac = parametric.parameters_values_df -test_class_instance.sampling_lhs(num_samples=3) -temp_lhs = test_class_instance.parameters_values_df +parametric.sampling_lhs(num_samples=3) +temp_lhs = parametric.parameters_values_df -# test_class_instance.sampling_full_set() -# temp_full_set = test_class_instance.parameters_values_df +# parametric.sampling_full_set() +# temp_full_set = parametric.parameters_values_df #todo try to return series of pmot, acst, ahst and optemp and plot them in facetgrid -outputs = test_class_instance.run_parametric_simulation( +outputs = parametric.run_parametric_simulation( epws=[ 'Sydney.epw', 'Seville.epw' diff --git a/parametric_simulation_usage_v00_accim_predef.py b/parametric_simulation_usage_v00_accim_predef.py index 8e485e9..3137ac4 100644 --- a/parametric_simulation_usage_v00_accim_predef.py +++ b/parametric_simulation_usage_v00_accim_predef.py @@ -125,7 +125,7 @@ accis_params_dict=accis_parameters, # additional_params=other_parameters ) -## + # Let's set the problem parametric.set_problem() @@ -139,7 +139,7 @@ parametric.sampling_full_set() temp_full_set = parametric.parameters_values_df -outputs = parametric.run_parametric_simulation( +parametric.run_parametric_simulation( epws=[ 'Sydney.epw', 'Seville.epw' @@ -154,6 +154,7 @@ # outputs.to_excel('WIP_outputs.xlsx') +[i.name for i in parametric.sim_outputs] ## @@ -189,17 +190,21 @@ ## +#Let's make a copy of the dataframe to not to modify the original one df = parametric.outputs_param_simulation_hourly.copy() -set(df['epw']) + +# The name of the column for the Running mean outdoor temperature is very long, so let's save it in the variable rmot: rmot = [i for i in df.columns if 'Running Average' in i][0] -optemp = [i for i in df.columns if 'Zone Operative Temperature' in i][0] +#Let's remove the columns where value is the same for all rows for c in df.columns: if len(set(df[c])) == 1: df = df.drop(columns=[c]) -df = df.drop(columns=['hour', 'datetime']) +#Now let's remove the hour and datetime columns, since will +df = df.drop(columns=['hour']) -df = df.melt(id_vars=['CAT', 'epw', rmot]) +# Now let's reshape the df for plotting purposes +df = df.melt(id_vars=['datetime', 'CAT', 'epw', rmot]) ## @@ -219,7 +224,7 @@ alpha=0.5 ) g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)') -g.add_legend() +g.add_legend(loc='lower center') for lh in g._legend.legend_handles: lh.set_markersize(5) @@ -234,7 +239,30 @@ # markerscale=2 # ) +plt.tight_layout() + + +## +import seaborn as sns +g = sns.FacetGrid( + data=df, + row='CAT', + col='epw' +) +g.map_dataframe( + sns.lineplot, + x='datetime', + y='value', + hue='variable', + # s=1, + # alpha=0.5 +) +g.set_axis_labels('RMOT (°C)', 'Indoor Operative Temperature (°C)') +g.add_legend(loc='lower center') +plt.tight_layout() +# for lh in g._legend.legend_handles: +# lh.set_markersize(5) ##