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Article: A Novel Combined Data-Driven Approach for Electricity Theft Detection

TitleA Novel Combined Data-Driven Approach for Electricity Theft Detection
Authors
KeywordsCyber security
data mining
electricity theft detection
energy internet
nontechnical loss (NTL)
smart meter
Issue Date2019
Citation
IEEE Transactions on Industrial Informatics, 2019, v. 15, n. 3, p. 1809-1819 How to Cite?
AbstractThe two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information, which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the maximum information coefficient (MIC), which can find the correlations between the nontechnical loss and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.
Persistent Identifierhttp://hdl.handle.net/10722/308766
ISSN
2023 Impact Factor: 11.7
2023 SCImago Journal Rankings: 4.420
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Kedi-
dc.contributor.authorChen, Qixin-
dc.contributor.authorWang, Yi-
dc.contributor.authorKang, Chongqing-
dc.contributor.authorXia, Qing-
dc.date.accessioned2021-12-08T07:50:05Z-
dc.date.available2021-12-08T07:50:05Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Industrial Informatics, 2019, v. 15, n. 3, p. 1809-1819-
dc.identifier.issn1551-3203-
dc.identifier.urihttp://hdl.handle.net/10722/308766-
dc.description.abstractThe two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information, which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the maximum information coefficient (MIC), which can find the correlations between the nontechnical loss and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Industrial Informatics-
dc.subjectCyber security-
dc.subjectdata mining-
dc.subjectelectricity theft detection-
dc.subjectenergy internet-
dc.subjectnontechnical loss (NTL)-
dc.subjectsmart meter-
dc.titleA Novel Combined Data-Driven Approach for Electricity Theft Detection-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TII.2018.2873814-
dc.identifier.scopuseid_2-s2.0-85054472145-
dc.identifier.volume15-
dc.identifier.issue3-
dc.identifier.spage1809-
dc.identifier.epage1819-
dc.identifier.isiWOS:000460580100054-

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