JADE-Based Feature Selection for Non-technical Losses Detection

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dc.contributor.authorPereira, Clayton Reginaldo [UNESP]
dc.contributor.authorPassos, Leandro Aparecido [UNESP]
dc.contributor.authorRodrigues, Douglas
dc.contributor.authorde Souza, André Nunes [UNESP]
dc.contributor.authorPapa, João P. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2020-12-12T02:27:07Z
dc.date.available2020-12-12T02:27:07Z
dc.date.issued2019-01-01
dc.description.abstractNowadays, non-technical losses, usually caused by thefts and cheats in the energy system distribution, are among the most significant problems an electric power company has to face. Several actions are employed striving to contain or reduce the implications of the conducts mentioned above, especially using automatic identification techniques. However, selecting a proper set of features in a large dataset is essential for successful detection rate, though it does not represent a straightforward task. This paper proposes a modification of JADE, an efficient adaptive differential evolution algorithm, for selecting the most representative features concerning the task of computer-assisted non-technical losses detection. Experiments on general-purpose datasets also evidence the robustness of the proposed approach.en
dc.description.affiliationSchool of Sciences UNESP - São Paulo State University
dc.description.affiliationDepartment of Computing UFSCar - Federal University of São Carlos
dc.description.affiliationUnespSchool of Sciences UNESP - São Paulo State University
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdFAPESP: 2017/02286-0
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdCNPq: 427968/2018-6
dc.format.extent141-156
dc.identifierhttp://dx.doi.org/10.1007/978-3-030-32040-9_16
dc.identifier.citationLecture Notes in Computational Vision and Biomechanics, v. 34, p. 141-156.
dc.identifier.doi10.1007/978-3-030-32040-9_16
dc.identifier.issn2212-9413
dc.identifier.issn2212-9391
dc.identifier.scopus2-s2.0-85073170103
dc.identifier.urihttp://hdl.handle.net/11449/201219
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computational Vision and Biomechanics
dc.sourceScopus
dc.subjectAdaptive differential evolution
dc.subjectEnergy theft detection
dc.subjectFeature selection
dc.subjectJADE
dc.titleJADE-Based Feature Selection for Non-technical Losses Detectionen
dc.typeCapítulo de livro
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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Bauru - FC - Faculdade de Ciências