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Conference Paper: Electricity theft detecting based on density-clustering method

TitleElectricity theft detecting based on density-clustering method
Authors
KeywordsAbnormal detection
Density- based clustering
Electricity theft
Smart meter data
Issue Date2018
Citation
2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Auckland, New Zealand, 4-7 December 2017. In Conference Proceedings, 2018, p. 182-187 How to Cite?
AbstractNowadays, the problem of electricity theft and tampered smart meter data is causing widespread concern. Customer load profiles collected from smart meters can help detect abnormal electricity users and identify electricity theft. In this paper, a density-based electricity theft detection method is proposed to find out abnormal electricity patterns. Several malicious types are used to test the validation of the proposed method. Comparisons with k-means clustering, Gaussian mixture model (GMM) clustering and density-based spatial clustering of applications with noise (DBSCAN) are also con ducted. Numerical experiments show that the proposed method outperforms other methods in almost all the theft types.
Persistent Identifierhttp://hdl.handle.net/10722/308759
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Kedi-
dc.contributor.authorWang, Yi-
dc.contributor.authorChen, Qixin-
dc.contributor.authorLi, Yuanpeng-
dc.date.accessioned2021-12-08T07:50:04Z-
dc.date.available2021-12-08T07:50:04Z-
dc.date.issued2018-
dc.identifier.citation2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Auckland, New Zealand, 4-7 December 2017. In Conference Proceedings, 2018, p. 182-187-
dc.identifier.urihttp://hdl.handle.net/10722/308759-
dc.description.abstractNowadays, the problem of electricity theft and tampered smart meter data is causing widespread concern. Customer load profiles collected from smart meters can help detect abnormal electricity users and identify electricity theft. In this paper, a density-based electricity theft detection method is proposed to find out abnormal electricity patterns. Several malicious types are used to test the validation of the proposed method. Comparisons with k-means clustering, Gaussian mixture model (GMM) clustering and density-based spatial clustering of applications with noise (DBSCAN) are also con ducted. Numerical experiments show that the proposed method outperforms other methods in almost all the theft types.-
dc.languageeng-
dc.relation.ispartof2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)-
dc.subjectAbnormal detection-
dc.subjectDensity- based clustering-
dc.subjectElectricity theft-
dc.subjectSmart meter data-
dc.titleElectricity theft detecting based on density-clustering method-
dc.typeConference_Paper-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/ISGT-Asia.2017.8378347-
dc.identifier.scopuseid_2-s2.0-85049944326-
dc.identifier.spage182-
dc.identifier.epage187-
dc.identifier.isiWOS:000435854300034-

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