eensighttool for measurement and verification of energy efficiency improvements
eensight Python package implements the measurement and verification (M&V) methodology that has been developed by the H2020 project SENSEI - Smart Energy Services to Improve the Energy Efficiency of the European Building Stock.
The online book Rethinking Measurement and Verification of Energy Savings (accessible here) explains in detail both the methodology and its implementation.
eensight can be installed by pip:
pip install eensight
All the functionality in
eensight is organized around data pipelines. Each pipeline consumes data and other artifacts (such as models) produced by a previous pipeline, and produces new data and artifacts for its successor pipelines.
There are four (4) pipelines in
eensight. The names of the pipelines and the associations between pipelines and namespaces are summarized below:
The primary way of using
eensight is through the command line. The first argument is always the name of the pipeline to run, such as:
eensight run predict --namespace train
eensight run --help
prints the documentation for all the options that can be passed to the command line.
The pipelines of
eensight are separate from the methods that implement them, so that the latter can be used directly:
import pandas as pd from eensight.methods.prediction.baseline import UsagePredictor from eensight.methods.prediction.activity import estimate_activity non_occ_features = ["temperature", "dew point temperature"] activity = estimate_activity( X, y, non_occ_features=non_occ_features, exog="temperature", assume_hurdle=False, ) X_act = pd.concat([X, activity.to_frame("activity")], axis=1) model = UsagePredictor(skip_calendar=True).fit(X_act, y)