Code for NILM experiments using Neural Networks. Uses Keras/Tensorflow and the NILMTK.
Metadata for the UK Domestic Appliance-Level Electricity (UK-DALE) dataset
The super-state hidden Markov model disaggregator that uses a sparse Viterbi algorithm for decoding. This project contains the source code that was used for the IEEE Transactions on Smart Grid journal paper.
Hidden Markov Models in Python, with scikit-learn like API http://hmmlearn.readthedocs.org
notebooks associated with the paper results of the NILMTK's Buildsys 2019 paper.
The state-of-the-art algorithms for the task of energy disaggregation implemented using NILMTK's Rapid Experimentation API.
The ENERTALK Dataset, 15 Hz Electricity Consumption Data from 22 Houses in Korea
A User-Oriented Energy Monitor to Enhance Energy Efficiency in Households
Sequence-to-point learning for non-intrusive load monitoring (energy disaggregation)
Latent Bayesian melding for non-intrusive load monitoring (energy disaggregation)
A schema for modelling meters, measurements, appliances, buildings etc
