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AuthorKharal, Ammar Yousaf
AuthorKhalid, Hassan Abdullah
AuthorGastli, Adel
AuthorGuerrero, Josep M.
Available date2022-11-23T11:25:31Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
Resource2-s2.0-85107349366
URIhttp://dx.doi.org/10.1109/ACCESS.2021.3085501
URIhttp://hdl.handle.net/10576/36614
AbstractAccording to statistics, developing countries all over the world have suffered significant non-technical losses (NTLs) both in natural gas and electricity distribution. NTLs are thought of as energy that is consumed but not billed e.g., theft, meter tampering, meter reversing, etc. The adaptation of smart metering technology has enabled much of the developed world to significantly reduce their NTLs. Also, the recent advancements in machine learning and data analytics have enabled a further reduction in these losses. However, these solutions are not directly applicable to developing countries because of their infrastructure and manual data collection. This paper proposes a tailored solution based on machine learning to mitigate NTLs in developing countries. The proposed method is based on a multivariate Gaussian distribution framework to identify fraudulent consumers. It integrates novel features like social class stratification and the weather profile of an area. Thus, achieving a significant improvement in fraudulent consumer detection. This study has been done on a real dataset of consumers provided by the local power distribution companies that have been cross-validated by onsite inspection. The obtained results successfully identify fraudulent consumers with a maximum success rate of 75%. 2013 IEEE.
SponsorThis work was supported by the Qatar National Library.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectArtificial intelligence
data analytics
fraudulent consumer identification framework
machine learning
multivariate gaussian distribution
non-technical losses
TitleA Novel Features-Based Multivariate Gaussian Distribution Method for the Fraudulent Consumers Detection in the Power Utilities of Developing Countries
TypeArticle
Pagination81057-81067
Volume Number9
dc.accessType Open Access


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