On the Study of Commercial Losses in Brazil: A Binary Black Hole Algorithm for Theft Characterization
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
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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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/openAccess2024-06-28T13:34:23Zoai:repositorio.unesp.br:11449/163877Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:49:37.082976Repositório 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_ |
1808128986451017728 |