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A two stage approach to electricity theft detection in AMI using deep learning

Lookup NU author(s): Dr Mansoureh Zangiabadi


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© 2023Electricity fraud as a noteworthy portion of non-technical losses has continuously been a worldwide concern. On the other hand, once a fraud is detected, on-site inspection is necessary for final verification. Since inspecting all metering data is laborious and expensive, utilities constantly try to narrow the inspection to instances with a higher odds of fraud. The rise of smart meters has introduced AI-based methods to reduce the inspection's scope by leveraging consumption patterns. However, their performances are limited due to the insufficiency of malicious samples, known as data imbalance. In the following investigation, a two-stage deep-learning-based model is provided for detecting energy fraud using metering data. first, a deep architecture is developed to model suspicious behaviour of fraudulent customers; which is used to handle the problem of data imbalance by predicting possible theft scenarios for normal consumers. then, A DNN network is developed to distinguishes normal and suspicious consumers. Consideration use of clustering and deep learning, as well as concurrent use of consumption and livelihood data, has enabled the algorithm to understand customer's behaviour and not fall against non-malicious changes in consumption patterns. Evaluation of the following method on a repository with 6000 real-world metering data demonstrates its high performance.

Publication metadata

Author(s): Emadaleslami M, Haghifam M-R, Zangiabadi M

Publication type: Article

Publication status: Published

Journal: International Journal of Electrical Power and Energy Systems

Year: 2023

Volume: 150

Print publication date: 01/08/2023

Online publication date: 20/03/2023

Acceptance date: 09/03/2023

ISSN (print): 0142-0615

ISSN (electronic): 1879-3517

Publisher: Elsevier Ltd


DOI: 10.1016/j.ijepes.2023.109088


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