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Package for Measurement and Verification

Project description

Mave is a tool for automated measurement and verification. At it's most simple,
the aim is to read energy consumption data from before and after a retrofit
(pre-retrofit and post-retrofit data) and to predict how much energy the
retrofit saved. Mave does this by training a model to the data and using
the best model to predict energy consumption during a different period.

Assuming all of the dependencies are installed, install mave from source using:
python install

Alternatively, install mave from pypi using:
pip install mave --no-deps

If you run into trouble with the dependencies, see the
[Installation page of the wiki](

Try mave out on an example file using any of the examples methods below.
Each of these will build a model on the dataset contained in ex3.csv, the
difference between the three approaches is in how the post-retrofit period
is defined.
mave ex3.csv
This assumes the last 25% of the file represents the postretrofit period as the
default value of the 'test_size' argument is 0.25.
mave ex3.csv -ts 0.45
This uses the 'ts' or 'test_size' argument to explicitly specify the fraction
of the file to use. In this example the last 45% of the file represents the
postretrofit period.
mave ex3.csv -cp "1/11/2013 00:00"
This example uses the 'cp' or 'changepoint' argument to explicitly define the
date at which the post retrofit period begins. This overrides the 'test-size'
value. In this case all data on or after Jan 11, 2013 at 00:00 represents the
post-retrofit period.

Mave has many configurable options (e.g. -v for verbose output) which can be
passed as command line arguments or using a separate configuration file.
The configuration file also allows many other advanced modeling options, such
as specifying multiple different periods in the file to use as pre-retrofit or
post-retrofit, or periods to ignore entirely. Review the wiki documentation for
more details.

To cite this tool:
Paul Raftery & Tyler Hoyt, 2015, Mave: software automated Measurement and Verification.
Center for the Built Environment, University of California Berkeley,

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