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Jupyter Notebook Python
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disaggregate Modified Nov 6, 2019
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This repository contains all the state-of-the-art algorithms for the task of energy disaggregation implemented using NILMTK's Rapid Experimentation API

Installation Details

Currently, we are still working on developing a conda package, which might take some time to develop. In the meanwhile, you can install this by cloning the repository in the Lib/Site-packages in your environment. Rename the directory to nilmtk_contrib. Refer to this notebook for using the nilmtk-contrib algorithms, using the NILMTK-API.


Scikit-learn>=0.21 Keras>=2.2.4 Cvxpy>=1.0.0 NILMTK-0.3

Note: For faster computation of neural-networks, it is suggested that you install keras-gpu, since it can take advantage of GPUs. The algorithms AFHMM, AFHMM_SAC and DSC are CPU intensive, use a system with good CPU for these algorithms.

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