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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Andrey Pazderin & Firuz Kamalov & Pavel Y. Gubin & Murodbek Safaraliev & Vladislav Samoylenko & Nikita Mukhlynin & Ismoil Odinaev & Inga Zicmane, 2023. "Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review," Energies, MDPI, vol. 16(21), pages 1-33, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7460-:d:1275094
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    References listed on IDEAS

    as
    1. Yurtseven, Çağlar, 2015. "The causes of electricity theft: An econometric analysis of the case of Turkey," Utilities Policy, Elsevier, vol. 37(C), pages 70-78.
    2. Gaur, Vasundhara & Gupta, Eshita, 2016. "The determinants of electricity theft: An empirical analysis of Indian states," Energy Policy, Elsevier, vol. 93(C), pages 127-136.
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