NILMTK: an open source toolkit for non-intrusive load monitoring

By Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William John Knottenbelt, Alex Rogers, Amarjeet Singh, Mani B. Srivastava. Energy-Efficient Computing and Networking, 2014

528 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/72007365582175f8c07d1cecd655cc1c2b44e6fb

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Citations in the corpus

Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering (2017, methodology): “To handle most of the datasets, we rely on the NILMTK framework [39], which has been developed to study energy disaggregation algorithms (NALM).”
Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives (2021, background): “Furthermore, this will support the foundations for future anomaly detection competitions [254].”
The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, methodology): “The framework, called NILM-Eval, is similar in scope to the recently presented NILMTK framework [6] and aims at allowing researchers to run comprehensive performance evaluations of NILM algorithms.”
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation (2015, methodology): “For more complex appliances such as washing machines whose power demand can drop below threshold for short periods during a cycle, NILMTK ignores short periods of sub-threshold power demand.”