The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
NILMTK: an open source toolkit for non-intrusive load monitoring
Open accessAbstract
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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Scikit-learn: Machine Learning in Python
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Nonintrusive appliance load monitoring
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Factorial Hidden Markov Models
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Self-citationUnsupervised Disaggregation of Low Frequency Power Measurements
Kim, H., Marwah, M., Arlitt, M. F., Lyon, G., & Han, J. (2011) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972818.64 · Cited by 659
Is disaggregation the holy grail of energy efficiency? The case of electricity
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Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
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Cited by
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Kelly, J., & Knottenbelt, W. (2015) · Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments · doi:10.1145/2821650.2821672 · Cited by 891
Self-citationHimeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., & Amira, A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116601 · Cited by 660
New Frontiers in IoT: Networking, Systems, Reliability, and Security Challenges
Bagchi, S., Abdelzaher, T. F., Govindan, R., Shenoy, P., Atrey, A., Ghosh, P., & Xu, R. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3007690 · Cited by 72
Metadata
- Authors
- Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., & Srivastava, M.
- Venue
- Proceedings of the 5th international conference on Future energy systems
- Publisher
- ACM
- Published
- 2014-06-11
- Pages
- 265-276
- OpenAlex
- W2097481773
- Cited by
- 502
- References
- 21
- FWCI
- 44.29
- Type
- conference paper
- Language
- en
- Open access
- green
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- Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., & Srivastava, M. (2014). NILMTK: an open source toolkit for non-intrusive load monitoring. Proceedings of the 5th international conference on Future energy systems. https://doi.org/10.1145/2602044.2602051
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