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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Bibliographic Details
Published in:	Electric power systems research Vol. 199; p. 107414
Main Authors:	Verma, Sagar, Singh, Shikha, Majumdar, Angshul
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.10.2021 Elsevier Science Ltd Elsevier
Subjects:	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 Deep learning Non-intrusive load monitoring LSTM Autoencoder Energy disaggregation
ISSN:	0378-7796, 1873-2046
Online Access:	Get full text
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