New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Página do item simplificado
dc.contributor.authorOba Ramos, Caio Cesar
dc.contributor.authorde Souza, Andre Nunes
dc.contributor.authorFalcao, Alexandre Xavier
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:25:59Z
dc.date.available2014-05-20T13:25:59Z
dc.date.issued2012-01-01
dc.description.abstractAlthough nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.en
dc.description.affiliationUniv São Paulo, Dept Elect Engn, BR-05508970 São Paulo, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Comp, BR-13083852 São Paulo, Brazil
dc.description.affiliationUNESP Univ Estadual Paulista, Dept Comp, BR-17033360 São Paulo, Brazil
dc.description.affiliationUnespUNESP Univ Estadual Paulista, Dept Comp, BR-17033360 São Paulo, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 09/16206-1
dc.format.extent140-146
dc.identifierhttp://dx.doi.org/10.1109/TPWRD.2011.2170182
dc.identifier.citationIEEE Transactions on Power Delivery. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 27, n. 1, p. 140-146, 2012.
dc.identifier.doi10.1109/TPWRD.2011.2170182
dc.identifier.issn0885-8977
dc.identifier.lattes9039182932747194
dc.identifier.urihttp://hdl.handle.net/11449/8305
dc.identifier.wosWOS:000298380600016
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofIEEE Transactions on Power Delivery
dc.relation.ispartofjcr3.350
dc.relation.ispartofsjr1,814
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectFeature selectionen
dc.subjectgravitational search algorithmen
dc.subjectharmony searchen
dc.subjectnontechnical lossesen
dc.subjectoptimum-path foresten
dc.subjectparticle swarm optimizationen
dc.subjectpattern recognitionen
dc.titleNew Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selectionen
dc.typeArtigo
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIEEE-Inst Electrical Electronics Engineers Inc
unesp.author.lattes9039182932747194
unesp.author.lattes8212775960494686[2]
unesp.author.orcid0000-0002-6494-7514[4]
unesp.author.orcid0000-0002-8617-5404[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

Arquivos

Licença do Pacote
Agora exibindo 1 - 2 de 2
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:

Coleções

Bauru - FC - Faculdade de Ciências