This repository contains all the state-of-the-art algorithms for the task of energy disaggregation implemented using NILMTK's Rapid Experimentation API
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.