Multi-label LSTM autoencoder for non-intrusive appliance load monitoring
•Proposed a new approach to NILM.•LSTM autoencoder fuses dynamic and static modeling paradigms.•Improves over the state-of-the-art algorithms. This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are inheren...
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| Vydáno v: | Electric power systems research Ročník 199; s. 107414 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.10.2021
Elsevier Science Ltd Elsevier |
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| ISSN: | 0378-7796, 1873-2046 |
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| Abstract | •Proposed a new approach to NILM.•LSTM autoencoder fuses dynamic and static modeling paradigms.•Improves over the state-of-the-art algorithms.
This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are inherently time varying. However prior multi-label classification techniques could not model this dynamical behaviour. They used off-the-shelf algorithms for classifying static signals on NILM problems. This is the first work that shows how to account for the temporal variability of input signals in a multi-label classification framework. Results on benchmark datasets like REDD and Pecan Street show considerable improvement over several state-of-the-art shallow and deep techniques. |
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| AbstractList | This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are inherently time varying. However prior multi-label classification techniques could not model this dynamical behaviour. They used off-the-shelf algorithms for classifying static signals on NILM problems. This is the first work that shows how to account for the temporal variability of input signals in a multi-label classification framework. Results on benchmark datasets like REDD and Pecan Street show considerable improvement over several state-of-the-art shallow and deep techniques. •Proposed a new approach to NILM.•LSTM autoencoder fuses dynamic and static modeling paradigms.•Improves over the state-of-the-art algorithms. This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are inherently time varying. However prior multi-label classification techniques could not model this dynamical behaviour. They used off-the-shelf algorithms for classifying static signals on NILM problems. This is the first work that shows how to account for the temporal variability of input signals in a multi-label classification framework. Results on benchmark datasets like REDD and Pecan Street show considerable improvement over several state-of-the-art shallow and deep techniques. |
| ArticleNumber | 107414 |
| Author | Verma, Sagar Majumdar, Angshul Singh, Shikha |
| Author_xml | – sequence: 1 givenname: Sagar surname: Verma fullname: Verma, Sagar organization: CentraleSupelec, University Paris Saclay, France – sequence: 2 givenname: Shikha surname: Singh fullname: Singh, Shikha organization: Indraprastha Institute of Information Technology, India – sequence: 3 givenname: Angshul surname: Majumdar fullname: Majumdar, Angshul email: angshul@iiitd.ac.in, angshulm@ece.ubc.ca organization: Indraprastha Institute of Information Technology, India |
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| Cites_doi | 10.1109/44.31557 10.1109/TSG.2018.2815763 10.1145/2821650.2821672 10.1109/TSG.2017.2666220 10.1109/67.795138 10.1109/TSG.2016.2584581 10.1016/j.enbuild.2018.10.030 10.1109/5.192069 10.1016/j.epsr.2019.01.034 10.3390/electronics7100235 10.1016/j.epsr.2019.105961 10.1109/TII.2014.2361288 10.1609/aaai.v33i01.33011150 10.1109/TSG.2018.2865702 |
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| Keywords | Deep learning Non-intrusive load monitoring LSTM Autoencoder Energy disaggregation |
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Inf. doi: 10.1109/TII.2014.2361288 – start-page: 1150 year: 2019 ident: 10.1016/j.epsr.2021.107414_bib0026 article-title: Subtask gated networks for non-intrusive load monitoring publication-title: AAAI doi: 10.1609/aaai.v33i01.33011150 – start-page: 1472 year: 2012 ident: 10.1016/j.epsr.2021.107414_bib0006 article-title: Approximate inference in additive factorial hmms with application to energy disaggregation publication-title: AISTATS – start-page: 8345 year: 2019 ident: 10.1016/j.epsr.2021.107414_bib0019 article-title: Multi label restricted Boltzmann machine for non-intrusive load monitoring publication-title: IEEE ICASSP – volume: 10 start-page: 4615 issue: 4 year: 2019 ident: 10.1016/j.epsr.2021.107414_bib0024 article-title: Residential household non-intrusive load monitoring via graph-based multi-label semi-supervised learning publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2018.2865702 |
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This work... This work follows the multi-label classification based paradigm for non-intrusive load monitoring (NILM). Power consumption signals used for NILM are... |
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| SubjectTerms | Algorithms Artificial Intelligence Autoencoder Computer Science Datasets Deep learning Electric power Energy consumption Energy disaggregation Engineering Sciences LSTM Monitoring systems Non-intrusive load monitoring Power consumption Signal classification Signal monitoring |
| Title | Multi-label LSTM autoencoder for non-intrusive appliance load monitoring |
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