Knowledge distillation for scalable non-intrusive load monitoring
Tanoni, Giulia and Stankovic, Lina and Stankovic, Vladimir and Squartini, Stefano and Principi, Emanuele (2024) Knowledge distillation for scalable non-intrusive load monitoring. IEEE Transactions on Industrial Informatics, 20 (3). pp. 4710-4721. ISSN 1551-3203 (https://doi.org/10.1109/TII.2023.3328436)
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Abstract
Smart meters allow the grid to interface with individual buildings and extract detailed consumption information using nonintrusive load monitoring (NILM) algorithms applied to the acquired data. Deep neural networks, which represent the state of the art for NILM, are affected by scalability issues since they require high computational and memory resources, and by reduced performance when training and target domains mismatched. This article proposes a knowledge distillation approach for NILM, in particular for multilabel appliance classification, to reduce model complexity and improve generalization on unseen data domains. The approach uses weak supervision to reduce labeling effort, which is useful in practical scenarios. Experiments, conducted on U.K.-DALE and REFIT datasets, demonstrated that a low-complexity network can be obtained for deployment on edge devices while maintaining high performance on unseen data domains. The proposed approach outperformed benchmark methods in unseen target domains achieving a F1 -score 0.14 higher than a benchmark model 78 times more complex.
ORCID iDs
Tanoni, Giulia, Stankovic, Lina ORCID: https://orcid.org/0000-0002-8112-1976, Stankovic, Vladimir ORCID: https://orcid.org/0000-0002-1075-2420, Squartini, Stefano and Principi, Emanuele;-
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Item type: Article ID code: 87026 Dates: DateEvent1 March 2024Published9 November 2023Published Online17 October 2023AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science
Technology > Electrical engineering. Electronics Nuclear engineering > Electrical apparatus and materialsDepartment: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 23 Oct 2023 10:45 Last modified: 11 Nov 2024 14:07 URI: https://strathprints.strath.ac.uk/id/eprint/87026