Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review
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| Автор |
Pazderin, A.
Kamalov, F. Gubin, P. Y. Safaraliev, M. Samoylenko, V. Mukhlynin, N. Odinaev, I. Zicmane, I. |
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| Тематика |
DISTRIBUTION NETWORKS
ELECTRICAL ENERGY ACCOUNTING MACHINE LEARNING NEURAL NETWORKS NONTECHNICAL LOSSES OF ELECTRICAL ENERGY THEFT OF ELECTRICAL ENERGY ANOMALY DETECTION ELECTRIC LOSSES ELECTRIC NETWORK PARAMETERS ENERGY UTILIZATION MACHINE LEARNING ELECTRICAL ENERGY ELECTRICAL ENERGY ACCOUNTING ENERGY ACCOUNTING MACHINE-LEARNING NEURAL-NETWORKS NON-TECHNICAL LOSS NONTECHNICAL LOSS OF ELECTRICAL ENERGY THEFT OF ELECTRICAL ENERGY ELECTRIC POWER DISTRIBUTION |
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| Описание |
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. © 2023 by the authors.
Ministry of Education and Science of the Russian Federation, Minobrnauka: FEUZ-2023-0013 This work was partially supported by the Ministry of Science and Higher Education of the Russian Federation (through the basic part of the government mandate, project No. FEUZ-2023-0013). |
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| Дата |
2024-04-05T16:36:26Z
2024-04-05T16:36:26Z 2023 |
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| Тип |
Review
Review (info:eu-repo/semantics/review) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Pazderin, A, Kamalov, F, Gubin, P, Safaraliev, M, Samoylenko, V, Mukhlynin, N, Odinaev, I & Zicmane, I 2023, 'Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review', Energies, Том. 16, № 21, 7460. https://doi.org/10.3390/en16217460
Pazderin, A., Kamalov, F., Gubin, P., Safaraliev, M., Samoylenko, V., Mukhlynin, N., Odinaev, I., & 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 1996-1073 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176504516&doi=10.3390%2fen16217460&partnerID=40&md5=a1716f7a687cad2f0b8420afb11a0a3f https://www.mdpi.com/1996-1073/16/21/7460/pdf?version=1699284909 http://elar.urfu.ru/handle/10995/130954 10.3390/en16217460 85176504516 001100284800001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Multidisciplinary Digital Publishing Institute (MDPI)
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| Источник |
Energies
Energies |
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