OpenEEmeter is an open source toolkit for implementing and developing standard methods for calculating normalized metered energy consumption (NMEC) and avoided energy use. The OpenEEmeter library contains routines for estimating energy efficiency savings at the meter.
OpenEEmeter includes the reference implementation of the CalTRACK methods for estimating normalized metered energy savings. CalTRACK is a working group under the Energy Market Methods Consortium (EM2).
OpenEEmeter, as implemented in the eemeter package and its companion eeweather package, contains the most complete open source implementation of the CalTRACK methods, which specify a family of ways to calculate and aggregate estimates avoided energy use at a single meter particularly suitable for use in pay-for-performance (P4P) programs.
The eemeter package contains a toolkit, written in the Python language, which may help in implementing a CalTRACK compliant analysis (see CalTRACK Compliance in the documentation). eemeter contains a modular set of functions, parameters, and classes that can be configured to run the CalTRACK methods and close variants.
OpenEEmeter Features:
- Candidate model selection
- Data sufficiency checking
- Reference implementation of standard methods
- CalTRACK Daily Method
- CalTRACK Monthly Billing Method
- CalTRACK Hourly Method
- Flexible sources of temperature data. See EEweather.
- Model serialization
- First-class warnings reporting
- Pandas DataFrame support
- Visualization tools
OpenEEmeter was initially developed by Recurve and contributed to LF Energy in 2019.