NILMTK: an open source toolkit for non-intrusive load monitoring
Nipun Batra,John Kelly,Oliver Parson,Haimonti Dutta,William J. Knottenbelt,Alex Rogers,Amarjeet Singh,Mani Srivastava +7 more
- pp 265-276
TLDR
This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets, and demonstrates the range of reproducible analyses made possible by the toolkit.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.read more
Citations
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Journal ArticleDOI
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
TL;DR: This work presents UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances, which is the first open access UK dataset at this temporal resolution.
Proceedings ArticleDOI
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
TL;DR: Three deep neural network architectures are adapted to energy disaggregation and it is found that all three neural nets achieve better F1 scores than either combinatorial optimisation or factorial hidden Markov models and that the neural net algorithms generalise well to an unseen house.
Journal ArticleDOI
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes.
TL;DR: UK-DALE as mentioned in this paper is an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole house and at 1/6 kHz for individual appliances.
Proceedings ArticleDOI
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
TL;DR: In this article, the authors adapt three deep neural network architectures to energy disaggregation: Long Short Term Memory (LSTM), denoising autoencoders and a network which regresses the start time, end time and average power demand of each appliance activation.
Proceedings ArticleDOI
The ECO data set and the performance of non-intrusive load monitoring algorithms
TL;DR: This paper describes the design and implementation of a framework that significantly eases the evaluation of NILM algorithms using different data sets and parameter configurations, and demonstrates the use of the presented framework and data set through an extensive performance evaluation of four selected NilM algorithms.
References
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