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README.rst

EEmeter: tools for calculating metered energy savings

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EEmeter — an open source toolkit for implementing and developing standard methods for calculating normalized metered energy consumption (NMEC) and avoided energy use.

Background - why use the EEMeter library

At time of writing (Sept 2018), the OpenEEmeter, as implemented in the eemeter package and sister 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 langage which may help in implementing a CalTRACK compliant analysis.

It contains a modular set of of functions, parameters, and classes which can be configured to run the CalTRACK methods and close variants.

Note

Please keep in mind that use of the OpenEEmeter is neither necessary nor sufficient for compliance with the CalTRACK method specification. For example, while the CalTRACK methods set specific hard limits for the purpose of standardization and consistency, the EEmeter library can be configured to edit or entirely ignore those limits. This is becuase the emeter package is used not only for compliance with, but also for development of the CalTRACK methods.

Please also keep in mind that the EEmeter assumes that certain data cleaning tasks specified in the CalTRACK methods have occurred prior to usage with the eemeter. The package proactively exposes warnings to point out issues of this nature where possible.

Installation

EEmeter is a python package and can be installed with pip.

$ pip install eemeter

Features

  • Reference implementation of standard methods
    • CalTRACK Daily Method
    • CalTRACK Monthly Billing Method
    • CalTRACK Hourly Method
  • Flexible sources of temperature data. See EEweather.
  • Candidate model selection
  • Data sufficiency checking
  • Model serialization
  • First-class warnings reporting
  • Pandas dataframe support
  • Visualization tools

Roadmap for 2020 development

The OpenEEmeter project growth goals for the year fall into two categories:

  1. Community goals - we want help our community thrive and continue to grow.
  2. Technical goals - we want to keep building the library in new ways that make it as easy as possible to use.

Community goals

  1. Develop project documentation and tutorials

A number of users have expressed how hard it is to get started when tutorials are out of date. We will dedicate time and energy this year to help create high quality tutorials that build upon the API documentation and existing tutorials.

  1. Make it easier to contribute

As our user base grows, the need and desire for users to contribute back to the library also grows, and we want to make this as seamless as possible. This means writing and maintaining contribution guides, and creating checklists to guide users through the process.

Technical goals

  1. Implement new CalTRACK recommendations

The CalTRACK process continues to improve the underlying methods used in the OpenEEmeter. Our primary technical goal is to keep up with these changes and continue to be a resource for testing and experimentation during the CalTRACK methods setting process.

  1. Hourly model visualizations

The hourly methods implemented in the OpenEEMeter library are not yet packaged with high quality visualizations like the daily and billing methods are. As we build and package new visualizations with the library, more users will be able to understand, deploy, and contribute to the hourly methods.

  1. Weather normal and unusual scenarios

The EEweather package, which supports the OpenEEmeter, comes packaged with publicly available weather normal scenarios, but one feature that could help make that easier would be to package methods for creating custom weather year scenarios.

  1. Greater weather coverage

The weather station coverage in the EEweather package includes full coverage of US and Australia, but with some technical work, it could be expanded to include greater, or even worldwide coverage.

License

This project is licensed under [Apache 2.0](LICENSE).

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An open source python package for implementing and developing standard methods for calculating normalized metered energy consumption and avoided energy use.

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