Publication:
Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system

Simple item page
dc.citedby166
dc.contributor.authorNagi J.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorAhmed S.K.en_US
dc.contributor.authorNagi F.en_US
dc.contributor.authorid25825455100en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid25926812900en_US
dc.contributor.authorid56272534200en_US
dc.date.accessioned2023-12-29T07:48:02Z
dc.date.available2023-12-29T07:48:02Z
dc.date.issued2011
dc.description.abstractThis letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy if-then rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distribution's detection hitrate has increased from 60% to 72%, thus proving to be cost effective. � 2011 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5738432
dc.identifier.doi10.1109/TPWRD.2010.2055670
dc.identifier.epage1285
dc.identifier.issue2
dc.identifier.scopus2-s2.0-79953193105
dc.identifier.spage1284
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79953193105&doi=10.1109%2fTPWRD.2010.2055670&partnerID=40&md5=813cf138b87b5715fff8dbc7e9c897e9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30456
dc.identifier.volume26
dc.pagecount1
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Power Delivery
dc.subjectComputational intelligence system
dc.subjectfuzzy logic
dc.subjectnontechnical loss
dc.subjectpattern classification
dc.subjectArtificial intelligence
dc.subjectComputer crime
dc.subjectCrime
dc.subjectEngineering research
dc.subjectFuzzy systems
dc.subjectComputational intelligence
dc.subjectCost effective
dc.subjectDetection framework
dc.subjectElectricity theft
dc.subjectFraud detection
dc.subjectFuzzy if-then rules
dc.subjectFuzzy inference systems
dc.subjectHuman knowledge
dc.subjectNon-technical loss
dc.subjectpattern classification
dc.subjectPost-processing scheme
dc.subjectPower distributions
dc.subjectPower utility
dc.subjectFuzzy inference
dc.titleImproving SVM-based nontechnical loss detection in power utility using the fuzzy inference systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS