Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

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dc.contributor.authorPazderin, Andrey
dc.contributor.authorKamalov, Firuz
dc.contributor.authorGubin, Pavel Y.
dc.contributor.authorSafaraliev, Murodbek
dc.contributor.authorSamoylenko, Vladislav
dc.contributor.authorMukhlynin, Nikita
dc.contributor.authorOdinaev, Ismoil
dc.contributor.authorZicmane, Inga
dc.date.accessioned2023-12-26T07:55:35Z
dc.date.available2023-12-26T07:55:35Z
dc.date.copyright© 2023
dc.date.issued2023-11
dc.description.abstractNontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms. © 2023 by the authors.
dc.identifier.citationPazderin, A., Kamalov, F., Gubin, P. Y., Safaraliev, M., Samoylenko, V., Mukhlynin, N., ... & Zicmane, I. (2023). Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review. Energies, 16(21), 7460. https://doi.org/10.3390/en16217460
dc.identifier.issn19961073
dc.identifier.urihttps://doi.org/10.3390/en16217460
dc.identifier.urihttps://hdl.handle.net/20.500.12519/971
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relationAuthors Affiliations : Pazderin, A., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, P.O. Box 117781, Dubai, United Arab Emirates; Gubin, P.Y., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Safaraliev, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Samoylenko, V., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Mukhlynin, N., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Odinaev, I., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, 620002, Russian Federation; Zicmane, I., Faculty of Electrical and Environmental Engineering, Riga Technical University, Riga, 1048, Latvia
dc.relation.ispartofseriesEnergies; Volume 16, Issue 21
dc.rightsCreative Commons Attribution (CC BY) license
dc.rights.holderCopyright : © 2023 by the authors.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectdistribution networks
dc.subjectelectrical energy accounting
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectnontechnical losses of electrical energy
dc.subjecttheft of electrical energy
dc.titleData-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
dc.typeArticle

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