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Journal Articles ACM Transactions on Knowledge Discovery from Data (TKDD) Year : 2022

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

Abstract

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.
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Dates and versions

hal-03463403 , version 1 (02-12-2021)

Identifiers

Cite

Shikha Singh, Emilie Chouzenoux, Giovanni Chierchia, Angshul Majumdar. Multi-label Deep Convolutional Transform Learning for Non-intrusive Load Monitoring. ACM Transactions on Knowledge Discovery from Data (TKDD), 2022, 16 (5), pp.1-6. ⟨10.1145/3502729⟩. ⟨hal-03463403⟩
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