In this project, we have to develop accurate models of metered building energy usage in the following areas: chilled water, electric, hot water, and steam meters. The data comes from over 1,000 buildings over a three-year timeframe. With better estimates of these energy-saving investments, large scale investors and financial institutions will be more inclined to invest in this area to enable progress in building efficiencies.
Q: How much does it cost to cool a skyscraper in the summer? A: A lot! And not just in dollars, but in environmental impact.
Thankfully, significant investments are being made to improve building efficiencies to reduce costs and emissions. The question is, are the improvements working? That’s where you come in. Under pay-for-performance financing, the building owner makes payments based on the difference between their real energy consumption and what they would have used without any retrofits. The latter values have to come from a model. Current methods of estimation are fragmented and do not scale well. Some assume a specific meter type or don’t work with different building types.
Founded in 1894, ASHRAE serves to advance the arts and sciences of heating, ventilation, air conditioning refrigeration and their allied fields. ASHRAE members represent building system design and industrial process professionals around the world. With over 54,000 members serving in 132 countries, ASHRAE supports research, standards writing, publishing and continuing education - shaping tomorrow’s built environment today.
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train.csv
- building_id - Foreign key for the building metadata.
- meter - The meter id code. Read as {0: electricity, 1: chilledwater, 2: steam, 3: hotwater}. Not every building has all meter types.
- timestamp - When the measurement was taken
- meter_reading - The target variable. Energy consumption in kWh (or equivalent). Note that this is real data with measurement error, which we expect will impose a baseline level of modeling error. building_meta.csv
- site_id - Foreign key for the weather files.
- building_id - Foreign key for training.csv
- primary_use - Indicator of the primary category of activities for the building based on EnergyStar property type definitions
- square_feet - Gross floor area of the building
- year_built - Year building was opened
- floor_count - Number of floors of the building
- weather_[train/test].csv
- Weather data from a meteorological station as close as possible to the site.
site_id
- air_temperature - Degrees Celsius
- cloud_coverage - Portion of the sky covered in clouds, in oktas
- dew_temperature - Degrees Celsius
- precip_depth_1_hr - Millimeters
- sea_level_pressure - Millibar/hectopascals
- wind_direction - Compass direction (0-360)
- wind_speed - Meters per second test.csv
- The submission files use row numbers for ID codes in order to save space on the file uploads. test.csv has no feature data; it exists so you can get your predictions into the correct order.
- row_id - Row id for your submission file
- building_id - Building id code
- meter - The meter id code
- timestamp - Timestamps for the test data period