On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization

Detalhes bibliográficos
Autor(a) principal: Ramos, Caio C. O. [UNESP]
Data de Publicação: 2018
Outros Autores: Rodrigues, Douglas, Souza, Andre N. de [UNESP], Papa, Joao P. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/TSG.2016.2560801
http://hdl.handle.net/11449/163877
Resumo: According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.
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spelling On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft CharacterizationCommercial lossesblack hole algorithmoptimum-path forestAccording to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Sao Paulo State Univ, Dept Elect Engn, BR-17033360 Bauru, BrazilUniv Fed Sao Carlos, Comp Sci Dept, BR-13565905 Sao Carlos, SP, BrazilSao Paulo State Univ, Dept Comp, BR-17033360 Bauru, BrazilSao Paulo State Univ, Dept Elect Engn, BR-17033360 Bauru, BrazilSao Paulo State Univ, Dept Comp, BR-17033360 Bauru, BrazilFAPESP: 2014/16250-0CNPq: 306166/2014-3Ieee-inst Electrical Electronics Engineers IncUniversidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Ramos, Caio C. O. [UNESP]Rodrigues, DouglasSouza, Andre N. de [UNESP]Papa, Joao P. [UNESP]2018-11-26T17:48:16Z2018-11-26T17:48:16Z2018-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article676-683application/pdfhttp://dx.doi.org/10.1109/TSG.2016.2560801Ieee Transactions On Smart Grid. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 9, n. 2, p. 676-683, 2018.1949-3053http://hdl.handle.net/11449/16387710.1109/TSG.2016.2560801WOS:000425530800017WOS000425530800017.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Transactions On Smart Grid2,854info:eu-repo/semantics/openAccess2024-04-23T16:10:49Zoai:repositorio.unesp.br:11449/163877Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:10:49Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
title On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
spellingShingle On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
Ramos, Caio C. O. [UNESP]
Commercial losses
black hole algorithm
optimum-path forest
title_short On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
title_full On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
title_fullStr On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
title_full_unstemmed On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
title_sort On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
author Ramos, Caio C. O. [UNESP]
author_facet Ramos, Caio C. O. [UNESP]
Rodrigues, Douglas
Souza, Andre N. de [UNESP]
Papa, Joao P. [UNESP]
author_role author
author2 Rodrigues, Douglas
Souza, Andre N. de [UNESP]
Papa, Joao P. [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Federal de São Carlos (UFSCar)
dc.contributor.author.fl_str_mv Ramos, Caio C. O. [UNESP]
Rodrigues, Douglas
Souza, Andre N. de [UNESP]
Papa, Joao P. [UNESP]
dc.subject.por.fl_str_mv Commercial losses
black hole algorithm
optimum-path forest
topic Commercial losses
black hole algorithm
optimum-path forest
description According to The Brazilian Electricity Regulatory Agency, Brazil reached a loss of approximately U.S.$4 billion in commercial losses during 2011, which correspond to more than 27 000 GWh. The strengthening of the smart grid has brought a considerable amount of research that can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically detect commercial losses, but the problem of selecting the most representative features has not been widely discussed. In this paper, we make a parallel among the problem of commercial losses in Brazil and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm. The experimental setup is conducted over two private datasets (commercial and industrial) provided by a Brazilian electric utility, and it shows the importance of selecting the most relevant features in the context of theft characterization.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-26T17:48:16Z
2018-11-26T17:48:16Z
2018-03-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/TSG.2016.2560801
Ieee Transactions On Smart Grid. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 9, n. 2, p. 676-683, 2018.
1949-3053
http://hdl.handle.net/11449/163877
10.1109/TSG.2016.2560801
WOS:000425530800017
WOS000425530800017.pdf
url http://dx.doi.org/10.1109/TSG.2016.2560801
http://hdl.handle.net/11449/163877
identifier_str_mv Ieee Transactions On Smart Grid. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 9, n. 2, p. 676-683, 2018.
1949-3053
10.1109/TSG.2016.2560801
WOS:000425530800017
WOS000425530800017.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ieee Transactions On Smart Grid
2,854
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 676-683
application/pdf
dc.publisher.none.fl_str_mv Ieee-inst Electrical Electronics Engineers Inc
publisher.none.fl_str_mv Ieee-inst Electrical Electronics Engineers Inc
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1799965083176009728

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