Skip to main content

Open Energy Efficiency Meter

Project description

Build Status License Documentation Status PyPI Version Code Coverage Status Code Style

EEmeter — an open source python library for creating standardized models for predicting energy usage. These models are often used to calculate energy savings post demand side intervention (such as energy efficiency projects or demand response events).

Background - why use the EEMeter library

OpenEEmeter, as implemented in the eemeter package and sibling eeweather package builds upon the foundation of the CalTRACK Methods to provide free, open-source modeling tools to anyone seeking to model energy building usage. Eemeter models have been developed to meet or exceed the predictive capability of the CalTRACK models. These models adhere to a statistical approach, as opposed to an engineering approach, so that these models can be efficiently run on millions of meters at a time, while still providing accurate predictions.

Using default settings in eemeter will provide accurate and stable model predictions suitable for savings measurements from demand side interventions. Settings can be modified for research and development purposes, although the outputs of such models may no longer be an officially recognized measurement as these models have been verified by the OpenEEmeter Working Group.

Installation

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

$ pip install eemeter

Features

  • Models:

    • Energy Efficiency Daily Model

    • Energy Efficiency Billing (Monthly) Model

    • Energy Efficiency Hourly Model

    • Demand Response Hourly Model

  • Flexible sources of temperature data. See EEweather.

  • Data sufficiency checking

  • Model serialization

  • First-class warnings reporting

  • Pandas dataframe support

  • Visualization tools

Documentation

Documenation for this library can be found here. Additionally, within the repository, the scripts directory contains Jupyter Notebooks, which function as interactive examples.

Roadmap for 2024 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A number of users have expressed how hard it is to get started when tutorials are out of date.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 OpenEEmeter models

The OpenEEmeter Working Group continues to improve the underlying models in OpenEEmeter. We seek to continue to implement these models in a safe, tested manner so that these models may continue to be used within engineering pipelines effectively.

  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).

Other resources

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eemeter-4.0.7.tar.gz (1.4 MB view hashes)

Uploaded Source

Built Distribution

eemeter-4.0.7-py2.py3-none-any.whl (1.4 MB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page