NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

Abstract

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.

To appear in the fifth International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK. 2014

References in corpus (2)

• The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
• GraphLab: A New Framework For Parallel Machine Learning

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• On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
• Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
• Poster Abstract: Bits and Watts: Improving energy disaggregation performance using power line communication modems
• Energy Prediction using Spatiotemporal Pattern Networks
• Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
• Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring
• A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring
• Adversarial Energy Disaggregation for Non-intrusive Load Monitoring