Hybrid Non-Technical-Loss Detection in Fog-enabled Smart Grids
Journal article
Authors | Khan, H. M., Jabeen, F., Khan, A., Badawi, S. A., Maple, C. and Jeon, G. |
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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%. |
Keywords | NTL; ARIMA; Random forest Model; Energy fraud detection |
Year | 2024 |
Journal | Sustainable Energy Technologies and Assessments |
Journal citation | 65, pp. 1-9 |
Publisher | Elseiver |
ISSN | 2213-1388 |
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 |
Accepted author manuscript | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 04 May 2024 |
Publication process dates | |
Accepted | 14 Apr 2024 |
Deposited | 15 May 2024 |
Supplemental file | License File Access Level Open |
https://repository.derby.ac.uk/item/q6514/hybrid-non-technical-loss-detection-in-fog-enabled-smart-grids
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Accepted author manuscript
Supplemental file
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