Electrical consumers data clustering through optimum-path forest

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:26:20Z
dc.date.available2014-05-27T11:26:20Z
dc.date.created2014-05-27T11:26:20Z
dc.date.issued2011-12-21
dc.identifier2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011.
dc.identifierhttp://hdl.handle.net/11449/73077
dc.identifier10.1109/ISAP.2011.6082217
dc.identifier2-s2.0-83655211673
dc.identifier9039182932747194
dc.description.abstractNon-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.
dc.languageeng
dc.relation2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011
dc.rightsAcesso aberto
dc.sourceScopus
dc.subjectClustering
dc.subjectNon-technical Losses
dc.subjectOptimum-Path Forest
dc.subjectPattern Recognition
dc.subjectClustering techniques
dc.subjectData clustering
dc.subjectData sets
dc.subjectElectric power company
dc.subjectNon-technical loss
dc.subjectSpecific profile
dc.subjectClustering algorithms
dc.subjectCrime
dc.subjectData processing
dc.subjectElectric utilities
dc.subjectIndustry
dc.subjectIntelligent systems
dc.subjectPattern recognition
dc.subjectPower transmission
dc.subjectForestry
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectData Processing
dc.subjectElectric Power Transmission
dc.subjectElectricity
dc.subjectLosses
dc.titleElectrical consumers data clustering through optimum-path forest
dc.typeActas de congresos


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