A novel deep learning technique to detect electricity theft in smart grids using AlexNet

Khan, N; Shahid, Z; Alam, MM; Abu Bakar Sajak, A; Nazar, M; Mazliham, MS

A novel deep learning technique to detect electricity theft in smart grids using AlexNet

Khan, N Shahid, Z Alam, MM Abu Bakar Sajak, A Nazar, M Mazliham, MS

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Publisher:: Institution of Engineering and Technology (IET)
Publication Type:: Journal Article
Citation:: IET Renewable Power Generation, 2024, 18, (6), pp. 941-958
Issue Date:: 2024-04

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Field	Value	Language
dc.contributor.author	Khan, N
dc.contributor.author	Shahid, Z
dc.contributor.author	Alam, MM
dc.contributor.author	Abu Bakar Sajak, A
dc.contributor.author	Nazar, M
dc.contributor.author	Mazliham, MS
dc.date.accessioned	2025-03-11T00:08:57Z
dc.date.available	2025-03-11T00:08:57Z
dc.date.issued	2024-04
dc.identifier.citation	IET Renewable Power Generation, 2024, 18, (6), pp. 941-958
dc.identifier.issn	1752-1416
dc.identifier.issn	1752-1424
dc.identifier.uri	http://hdl.handle.net/10453/185646
dc.description.abstract	Abstract Electricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1 score, up to 86 , 89 , 86 , and 84 , respectively.
dc.language	en
dc.publisher	Institution of Engineering and Technology (IET)
dc.relation.ispartof	IET Renewable Power Generation
dc.relation.isbasedon	10.1049/rpg2.12846
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4011 Environmental engineering
dc.title	A novel deep learning technique to detect electricity theft in smart grids using AlexNet
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-03-11T00:08:55Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	6

Abstract:

Abstract Electricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1 score, up to 86 , 89 , 86 , and 84 , respectively.

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http://hdl.handle.net/10453/185646