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- Publisher Website: 10.1109/ISGT-Asia.2017.8378347
- Scopus: eid_2-s2.0-85049944326
- WOS: WOS:000435854300034
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Conference Paper: Electricity theft detecting based on density-clustering method
Title | Electricity theft detecting based on density-clustering method |
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Authors | |
Keywords | Abnormal detection Density- based clustering Electricity theft Smart meter data |
Issue Date | 2018 |
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? |
Abstract | Nowadays, 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 Identifier | http://hdl.handle.net/10722/308759 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zheng, Kedi | - |
dc.contributor.author | Wang, Yi | - |
dc.contributor.author | Chen, Qixin | - |
dc.contributor.author | Li, Yuanpeng | - |
dc.date.accessioned | 2021-12-08T07:50:04Z | - |
dc.date.available | 2021-12-08T07:50:04Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Auckland, New Zealand, 4-7 December 2017. In Conference Proceedings, 2018, p. 182-187 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308759 | - |
dc.description.abstract | Nowadays, 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.language | eng | - |
dc.relation.ispartof | 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia) | - |
dc.subject | Abnormal detection | - |
dc.subject | Density- based clustering | - |
dc.subject | Electricity theft | - |
dc.subject | Smart meter data | - |
dc.title | Electricity theft detecting based on density-clustering method | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1109/ISGT-Asia.2017.8378347 | - |
dc.identifier.scopus | eid_2-s2.0-85049944326 | - |
dc.identifier.spage | 182 | - |
dc.identifier.epage | 187 | - |
dc.identifier.isi | WOS:000435854300034 | - |