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

Andrey Pazderin, Firuz Kamalov, Pavel Y. Gubin, Murodbek Safaraliev, Vladislav Samoylenko, Nikita Mukhlynin, Ismoil Odinaev and Inga Zicmane ()
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Andrey Pazderin: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Firuz Kamalov: Department of Electrical Engineering, Canadian University Dubai, Dubai P.O. Box 117781, United Arab Emirates
Pavel Y. Gubin: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Murodbek Safaraliev: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Vladislav Samoylenko: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Nikita Mukhlynin: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Ismoil Odinaev: Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia
Inga Zicmane: Faculty of Electrical and Environmental Engineering, Riga Technical University, 1048 Riga, Latvia

Energies, 2023, vol. 16, issue 21, 1-33

Abstract: Nontechnical 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.

Keywords: nontechnical losses of electrical energy; theft of electrical energy; electrical energy accounting; distribution networks; machine learning; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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