Hybrid Non-Technical-Loss Detection in Fog-enabled Smart Grids

Journal article


Khan, H. M., Jabeen, F., Khan, A., Badawi, S. A., Maple, C. and Jeon, G. 2024. Hybrid Non-Technical-Loss Detection in Fog-enabled Smart Grids. Sustainable Energy Technologies and Assessments. 65, pp. 1-9. https://doi.org/10.1016/j.seta.2024.103775
AuthorsKhan, H. M., Jabeen, F., Khan, A., Badawi, S. A., Maple, C. and Jeon, G.
Abstract

Electricity theft is one of the major factors contributing to non-technical-losses (NTLs) in power distribution networks. NTL fraud includes frauds in which consumers profit unlawfully by manipulating smart meters (SMs), intruding networks, and so forth. This unlawful act not only undermines people’s efforts to conserve energy but also disrupts the regular billing cycle for power utilities, causing financial losses. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, two NTL Detection schemes are proposed for NTL fraud prediction. Both schemes employed the auto-regressive integrated moving average (ARIMA) and the machine learning technique to predict the consumer behavior fraud pattern efficiently. Furthermore, extensive simulations are conducted on real-world electricity consumption data sets, which show that the proposed schemes outperformed state-of-the-art solutions and achieved an accuracy of 98%, a precision of 98.6%, a recall of 98.2%, an AUC of 97.9%, and an F1 score of 98.4%.

KeywordsNTL; ARIMA; Random forest Model; Energy fraud detection
Year2024
JournalSustainable Energy Technologies and Assessments
Journal citation65, pp. 1-9
PublisherElseiver
ISSN2213-1388
Digital Object Identifier (DOI)https://doi.org/10.1016/j.seta.2024.103775
Web address (URL)https://www.sciencedirect.com/science/article/pii/S2213138824001711?via%3Dihub
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Output statusPublished
Publication dates
Online04 May 2024
Publication process dates
Accepted14 Apr 2024
Deposited15 May 2024
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