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A Scoping Review of Energy Load Disaggregation

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Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14116))

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Abstract

Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper conducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 s. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

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Acknowledgements

This paper is part of the project “Data-dreven smarte bygninger: data sandkasse og konkurrence” (Journalnummer: 64021-6025) by EUDP (Energy Technology Development and Demonstration Program), Denmark.

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Authors and Affiliations

  1. SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230, Odense, Denmark

    Balázs András Tolnai, Zheng Ma & Bo Nørregaard Jørgensen

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  1. Balázs András Tolnai
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  2. Zheng Ma
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Correspondence to Balázs András Tolnai .

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Editors and Affiliations

  1. Lucy Family Institute for Data and Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA—LIACC, University of Azores, Ponta Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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Tolnai, B.A., Ma, Z., Jørgensen, B.N. (2023). A Scoping Review of Energy Load Disaggregation. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_17

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