Merge pull request #5 from LBNL-JCI-ICF/exampleready

Exampleready

README.md

@@ -1,7 +1,7 @@
 # Building Efficiency Targeting Tool for Energy Retrofits (BETTER)
 
 ## Latest Releases
-Download the [latest release](https://github.com/LBNL-JCI-ICF/better/releases/) or see [installtion](#installation).
+Download the [latest release](https://github.com/LBNL-JCI-ICF/better/releases/) or see [installation](#installation).
 
 ## Background
 The lack of public-access, data-driven tools requiring minimal inputs and short run time to benchmark against peers, quantify energy/cost savings, and recommend energy efficiency (EE) improvements is one of the main barriers to capturing untapped EE opportunities in the United States and globally. To fill the gap, and simultaneously address the need for automated, cost-effective, and standardized EE assessment of large volumes of buildings in U.S. state and municipal benchmarking and disclosure programs, an automated, open-source, virtual building EE targeting tool is being developed by Lawrence Berkeley National Laboratory (LBNL), Johnson Controls (JCI), and ICF.
@@ -12,7 +12,6 @@ The tool is being developed under Cooperative Research and Development Agreement
 
 ## Getting Started
 
-
 ### Software Prerequisites
 BETTER is developed using Python 3.6. We recommend using Anaconda to manage Python environments. If you'd rather not install Anaconda, you can download Python 3.6 from [here](https://www.python.org/downloads/).
 
@@ -36,18 +35,18 @@ Please note that this tool was initially developed for an international market a
 Sample data for 10 buildings are included in `./data/portfolio.xlsx`. Metadata for each building to be analyzed should be entered in the “Metadata” tab, one row per building. Utility data for all fuel types should be entered on the “Utility” tab. Be sure to double check that the building ID, fuel type, and units are accurate for each utility bill entry, and be sure to save the file as `portfolio.xlsx`. Overwrite the file to suit your needs.
 
 #### Benchmark Statistics
-A sample benchmark statistic is provided in `./better/constants.py`. The team is working to create a database of U.S. buildings to allow the benchmarking and analysis of individual buildings.
+A sample benchmark statistic is provided in `./better/constants.py`. The team is working to create a database of U.S. buildings to allow the benchmarking and analysis of individual buildings. If you have a portfolio of at least 30 buildings, you may choose to benchmark individual buildings against your own data set. For smaller portfolios, your benchmark will be based on buildings in the demo. See “[How to Use](#how-to-use)” for information on how to select your benchmark data set.
 
 #### Weather Data
-Weather data is downloaded from the [NOAA website](https://governmentshutdown.noaa.gov/?page=gsod.html) for the building location. To use previously downloaded weather data at later runs set `cached_weather` to `False` in `run.py`.
+Weather data is downloaded from the [NOAA website](https://governmentshutdown.noaa.gov/?page=gsod.html) for the building location. To use previously downloaded weather data at later runs set `cached_weather` to `True` in `run.py`.
 
 ### Installation
 1. Download and install [Python >=3.6](https://www.python.org/downloads/)
 2. Download the source code from the [latest release](https://github.com/LBNL-JCI-ICF/better/releases/)
 3. Extract and navigate to the downloaded release 
 3. Install dependencies by clicking on `install.bat` or run `python setup.py install` on your cmd
 
-<i>Note: The current release is an alpha version. The tool will be packaged and setup files will be provided in future releases.</i> 
+*Note: The current release is an alpha version. The tool will be packaged and setup files will be provided in future releases.* 
 
 ## How to Use
 The focus of the development is the building energy benchmarking and EE targeting analytical core but not the user interface. To demonstrate the data input/output and the use of the tool.
@@ -57,17 +56,25 @@ The focus of the development is the building energy benchmarking and EE targetin
 2. Run `python demo.py`. It will run the sample of 10 buildings provided in `./data/portfolio.xslx`
 3. Output is stored in `./outputs`
 
+Once you have run the demo and familiarized yourself with the tool, you can use your own building data and follow the steps below to run analyses on either a single building or on a portfolio of buildings.
+
 ### Run Single Building
-1. Replace building information and utility data in the `./data/portfolio.xlsx` and save the file, or run as is for demo
-2. Open `./better/run.py` file using a text editor and
-   - For a single building: uncomment `run_single()`, set the target building id<br/>
-   - For a portfolio: uncomment `run_batch()`, set the start and end building id<br/>
-   - Set the saving target level.
-3. Run the analysis by running the `python run.py` from your cmd or terminal
+1.	Change building information and utility data in the `./data/portfolio.xlsx` and save the file.
+2.	Open `./better/run.py` file using a text editor and ensure that line **11** (`run_single(...)`) is uncommented, and line **13** (`run_batch(...)`) is commented out (i.e., has a “#” at the beginning of the line).
+3.	Set the target building ID based on the ID in `portfolio.xlsx` (e.g., `bldg_id = 1` – change the **1** to match the ID of the building you wish to analyze).
+4.	Set the saving target level (1 = conservative, 2 = nominal, 3 = aggressive) 
+5.	Run the analysis by running python run.py from your cmd or terminal
 
-## Interpreting Results
-The analysis results are in the `./outputs` folder. Single building reports and Portfolio reports.
+### Run Portfolio
+1.	Change building information and utility data in the `./data/portfolio.xlsx` and save the file.
+2.	Open ./better/run.py file using a text editor and ensure that line 11 (“run_single”) is commented out (i.e., has a “#” at the beginning of the line), and line 13 (“run_batch”) is uncommented.
+3.	Set the start and end building IDs based on the IDs in portfolio.xlsx (e.g., `start_id=1` and `end_id=20` – change the **1** and **20** to match the first and last IDs of the buildings you wish to analyze).
+4.	Set the saving target level (1 = conservative, 2 = nominal, 3 = aggressive)
+5.  Run the analysis by running the `python run.py` from your cmd or terminal
 
+
+## Interpreting Results
+The analysis results are in the `./outputs` folder. Comprehensive reports are provided in .html format for each individual building, and results are explained within those html files. For portfolio analyses, a separate Portfolio html output is also provided.
 ## Copyright
 
 Energy Efficiency Targeting Tool Copyright (c) 2018, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

better/building.py

@@ -98,7 +98,6 @@ def pre_process(self):
             self.annual_eui_electricity = round(
                 self.eui_daily_all_periods_electricity * constants.Constants.days_in_year, 2)
 
-
         if (hasattr(self, "utility_fossil_fuel") and hasattr(self.utility_fossil_fuel, "daily_kWh")):
             self.recent_annual_fossil_fuel_kWh = self.utility_fossil_fuel.recent_annual_consumption
             if self.utility_fossil_fuel.recent_annual_cost > 0: