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Open Energy Efficiency Meter

Project description

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 :any:`eeweather <eeweather:index>` 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 (see :ref:`caltrack-compliance`).
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.


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


$ pip install eemeter


- 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

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