NILM TK (Non-Intrusive Load Monitoring Toolkit)
License URL: http://nilmtk.github.io
Why a toolkit for NILM?
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.
What nilmtk provides
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. nilmtk includes:
parsers for a range of existing data sets (8 and counting)
a collection of preprocessing algorithms
a set of statistics for describing data sets
3 reference benchmark disaggregation algorithms and a suite of accuracy metrics
a common set of accuracy metrics
and much more!
Associated Papers
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Parson, O., Ghosh, S., Weal, M. J., & Rogers, A. (2014). An unsupervised training method for non-intrusive appliance load monitoring. Artificial Intelligence, 217, 1-19.
Get Bibtex Citation
@article{
eps367418,
volume = {217},
month = {December},
title = {An unsupervised training method for non-intrusive appliance load monitoring},
author = {Oliver Parson and Siddhartha Ghosh and Mark J. Weal and Alex Rogers},
year = {2014},
pages = {1--19},
journal = {Artificial Intelligence},
url = {http://eprints.soton.ac.uk/367418/},
abstract = {Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an unsupervised training method for non-intrusive monitoring which, unlike existing supervised approaches, does not require training data to be collected by sub-metering individual appliances, nor does it require appliances to be manually labelled for the households in which disaggregation is performed. Instead, we propose an approach which combines a one-off supervised learning process over existing labelled appliance data sets, with an unsupervised learning method over unlabelled household aggregate data. First, we propose an approach which uses the Tracebase data set to build probabilistic appliance models which generalise to previously unseen households, which we empirically evaluate through cross validation. Second, we use the Reference Energy Disaggregation Data set to evaluate the accuracy with which these general models can be tuned to the appliances within a specific household using only aggregate data. Our empirical evaluation demonstrates that general appliance models can be constructed using data from only a small number of appliances (typically 3-6 appliances), and furthermore that 28-99\% of the remaining behaviour which is specific to a single household can be learned using only aggregate data from existing smart meters.} }
-
Parson, O., Ghosh, S., Weal, M., & Rogers, A. (2012). Non-intrusive load monitoring using prior models of general appliance types. In Proceedings of theTwenty-Sixth Conference on Artificial Intelligence (AAAI-12).
Get Bibtex Citation
@inproceedings{
eps336812,
booktitle = {Proceedings of theTwenty-Sixth Conference on Artificial Intelligence (AAAI-12)},
month = {July},
title = {Non-intrusive load monitoring using prior models of general appliance types},
author = {Oliver Parson and Siddhartha Ghosh and Mark Weal and Alex Rogers},
year = {2012},
pages = {356--362},
url = {http://eprints.soton.ac.uk/336812/},
abstract = {Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback.} }
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Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., & Rogers, A., et al. (2014). {NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring}. In International Conference on Future Energy Systems (ACM e-Energy).
Get Bibtex Citation
@inproceedings{
eps364293,
booktitle = {International Conference on Future Energy Systems (ACM e-Energy)},
month = {April},
title = {{NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring}
},
author = {Nipun Batra and Jack Kelly and Oliver Parson and Haimonti Dutta and William Knottenbelt and Alex Rogers and Amarjeet Singh and Mani Srivastava},
year = {2014},
url = {http://eprints.soton.ac.uk/364293/},
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.} }
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Kelly, J., Batra, N., Parson, O., Dutta, H., Knottenbelt, W., & Rogers, A., et al. (2014). {NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets}. In The first ACM Workshop On Embedded Systems For Energy-Efficient Buildings at BuildSys 2014, {Memphis, USA.
Get Bibtex Citation
@Inproceedings{
kelly2014NILMTKv02,
Title = {{NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets}
},
Author = {Kelly, Jack and Batra, Nipun and Parson, Oliver and Dutta, Haimonti and Knottenbelt, William and Rogers, Alex and Singh, Amarjeet and Srivastava, Mani},
Booktitle = {The first ACM Workshop On Embedded Systems For Energy-Efficient Buildings at BuildSys 2014},
Year = {2014},
Doi = {10.1145/2674061.2675024},
Eprint = {1409.5908},
Eprinttype = {arXiv},
Address = {Memphis, USA}