Multi-label Deep Convolutional Transform Learning for Non-intrusive Load Monitoring

The objective of this letter is to propose a novel computational method to learn the state of an appliance (ON / OFF) given the aggregate power consumption recorded by the smart-meter. We formulate a multi-label classification problem where the classes correspond to the appliances. The proposed appr...

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Bibliographic Details
Published inACM transactions on knowledge discovery from data Vol. 16; no. 5; pp. 1 - 6
Main Authors Singh, Shikha, Chouzenoux, Emilie, Chierchia, Giovanni, Majumdar, Angshul
Format Journal Article
LanguageEnglish
Published ACM 01.10.2022
Subjects
Engineering Sciences
Signal and Image processing
non-intrusive load monitoring
representation learning
multi-label classification
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ISSN1556-4681
1556-472X
DOI10.1145/3502729

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Summary:The objective of this letter is to propose a novel computational method to learn the state of an appliance (ON / OFF) given the aggregate power consumption recorded by the smart-meter. We formulate a multi-label classification problem where the classes correspond to the appliances. The proposed approach is based on our recently introduced framework of convolutional transform learning. We propose a deep supervised version of it relying on an original multi-label cost. Comparisons with state-of-the-art techniques show that our proposed method improves over the benchmarks on popular non-intrusive load monitoring datasets.
ISSN:1556-4681
1556-472X
DOI:10.1145/3502729