KFU. Card publication. Integrating machine learning techniques to reduce commercial losses in the electricity sector: a review of practice in a grid organisation

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INTEGRATING MACHINE LEARNING TECHNIQUES TO REDUCE COMMERCIAL LOSSES IN THE ELECTRICITY SECTOR: A REVIEW OF PRACTICE IN A GRID ORGANISATION

Form of presentation

Articles in Russian journals and collections

Year of publication

2025

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Authors, employees of KFU

Kurbangalieva Dinara Lenarovna, author

Other authors

Kurbangaliev Timur , author

Usmanov Renat , author

Yakhin Shamil , author

Bibliographic description in the original language

Kurbangaliev T.R., Usmanov R.R., Yakhin Sh.R., Kurbangalieva D.L. Integrating machine learning techniques to reduce commercial losses in the electricity sector: a review of practice in a grid organisation / BUSINESS INFORMATICS - 2025 - Vol. 4

Annotation

Integrating technological advancements into smart grid optimize the power system. The dataset provided by smart metering devices allows to capture and transmit half-hourly electricity consumption profiles, events of poor quality of electricity, tampering attempts, consumption parameters and service events. Unfortunately, the problem of unaccounted consumption has remained relevant even with the use of smart meters. Moreover, today non-technical losses, as a consequence of intentional fraud consumption, are one of the main and very actual problems for electric power (EP) distribution companies. No doubt that electricity theft is a problem that affects the efficiency and profitability of power companies. However, the use of technologies can solve such problems. To solve this problem, this article suggests several solutions for classifying time series in order to detect anomalies in electricity consumption. The paper tried to prove the hypothesis about the applicability of artificial intelligence technologies based on neural network training for analyzing data on electricity consumption by consumers. The goal of our research was to develop our own neural network model for enhancing electricity theft. As a result of our work, it is concluded that Convolutional Neural Network (CNN) based on time series classification is an effective tool for finding and combating unaccounted consumption. These results highlight the potential of the proposed method for practical applications in the electricity market, as it can provide reliable and timely information for energy management. The theoretical significance of the study lies in the fact that the work attempts to use machine learning methods to improve theft-detection accuracy.

Keywords

Artificial intelligence, Smart metering devices, Digitalization, non-technical losses, neural network, machine learning

The name of the journal

BUSINESS INFORMATICS

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=320839&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Kurbangalieva Dinara Lenarovna	ru_RU
dc.contributor.author	Kurbangaliev Timur	ru_RU
dc.contributor.author	Usmanov Renat	ru_RU
dc.contributor.author	Yakhin Shamil	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Kurbangaliev T.R., Usmanov R.R., Yakhin Sh.R., Kurbangalieva D.L. Integrating machine learning techniques to reduce commercial losses in the electricity sector: a review of practice in a grid organisation / BUSINESS INFORMATICS - 2025 - Vol. 4	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=320839&p_lang=2	ru_RU
dc.description.abstract	BUSINESS INFORMATICS	ru_RU
dc.description.abstract	Integrating technological advancements into smart grid optimize the power system. The dataset provided by smart metering devices allows to capture and transmit half-hourly electricity consumption profiles, events of poor quality of electricity, tampering attempts, consumption parameters and service events. Unfortunately, the problem of unaccounted consumption has remained relevant even with the use of smart meters. Moreover, today non-technical losses, as a consequence of intentional fraud consumption, are one of the main and very actual problems for electric power (EP) distribution companies. No doubt that electricity theft is a problem that affects the efficiency and profitability of power companies. However, the use of technologies can solve such problems. To solve this problem, this article suggests several solutions for classifying time series in order to detect anomalies in electricity consumption. The paper tried to prove the hypothesis about the applicability of artificial intelligence technologies based on neural network training for analyzing data on electricity consumption by consumers. The goal of our research was to develop our own neural network model for enhancing electricity theft. As a result of our work, it is concluded that Convolutional Neural Network (CNN) based on time series classification is an effective tool for finding and combating unaccounted consumption. These results highlight the potential of the proposed method for practical applications in the electricity market, as it can provide reliable and timely information for energy management. The theoretical significance of the study lies in the fact that the work attempts to use machine learning methods to improve theft-detection accuracy.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Artificial intelligence	ru_RU
dc.subject	Smart metering devices	ru_RU
dc.subject	Digitalization	ru_RU
dc.subject	non-technical losses	ru_RU
dc.subject	neural network	ru_RU
dc.subject	machine learning	ru_RU
dc.title	Integrating machine learning techniques to reduce commercial losses in the electricity sector: a review of practice in a grid organisation	ru_RU
dc.type	Articles in Russian journals and collections	ru_RU