Black Hole Algorithm for Non-technical Losses Characterization

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Douglas
Data de Publicação: 2015
Outros Autores: Oba Ramos, Caio Cesar [UNESP], Souza, Andre Nunes de [UNESP], Papa, Joao Paulo [UNESP], Arnaud, A., Silveira, F., Garcia, L.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/161764
Resumo: With the consolidation of Smart Grids, a considerable amount of works can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically identify non-technical losses, but the problem of selecting the most representative features has not been widely discussed. In this work, we make a parallel among the problem of non-technical losses and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm (BHA). The experimental setup is conducted over two private datasets provided by a Brazilian electric power company, and it shows the importance of selecting the most relevant features in the context of nontechnical losses identification, as well as the suitability of BHA to this task.
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spelling Black Hole Algorithm for Non-technical Losses CharacterizationWith the consolidation of Smart Grids, a considerable amount of works can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically identify non-technical losses, but the problem of selecting the most representative features has not been widely discussed. In this work, we make a parallel among the problem of non-technical losses and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm (BHA). The experimental setup is conducted over two private datasets provided by a Brazilian electric power company, and it shows the importance of selecting the most relevant features in the context of nontechnical losses identification, as well as the suitability of BHA to this task.Univ Fed Sao Carlos, Dept Comp Sci, Sao Carlos, SP, BrazilUNESP Univ Estadual Paulista, Dept Elect Engn, Bauru, SP, BrazilUNESP Univ Estadual Paulista, Dept Comp, Bauru, SP, BrazilUNESP Univ Estadual Paulista, Dept Elect Engn, Bauru, SP, BrazilUNESP Univ Estadual Paulista, Dept Comp, Bauru, SP, BrazilIeeeUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Rodrigues, DouglasOba Ramos, Caio Cesar [UNESP]Souza, Andre Nunes de [UNESP]Papa, Joao Paulo [UNESP]Arnaud, A.Silveira, F.Garcia, L.2018-11-26T16:48:31Z2018-11-26T16:48:31Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject42015 Ieee 6th Latin American Symposium On Circuits & Systems (lascas). New York: Ieee, 4 p., 2015.2330-9954http://hdl.handle.net/11449/161764WOS:000380477800002Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Ieee 6th Latin American Symposium On Circuits & Systems (lascas)info:eu-repo/semantics/openAccess2024-04-23T16:11:26Zoai:repositorio.unesp.br:11449/161764Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:26Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Black Hole Algorithm for Non-technical Losses Characterization
title Black Hole Algorithm for Non-technical Losses Characterization
spellingShingle Black Hole Algorithm for Non-technical Losses Characterization
Rodrigues, Douglas
title_short Black Hole Algorithm for Non-technical Losses Characterization
title_full Black Hole Algorithm for Non-technical Losses Characterization
title_fullStr Black Hole Algorithm for Non-technical Losses Characterization
title_full_unstemmed Black Hole Algorithm for Non-technical Losses Characterization
title_sort Black Hole Algorithm for Non-technical Losses Characterization
author Rodrigues, Douglas
author_facet Rodrigues, Douglas
Oba Ramos, Caio Cesar [UNESP]
Souza, Andre Nunes de [UNESP]
Papa, Joao Paulo [UNESP]
Arnaud, A.
Silveira, F.
Garcia, L.
author_role author
author2 Oba Ramos, Caio Cesar [UNESP]
Souza, Andre Nunes de [UNESP]
Papa, Joao Paulo [UNESP]
Arnaud, A.
Silveira, F.
Garcia, L.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Federal de São Carlos (UFSCar)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Rodrigues, Douglas
Oba Ramos, Caio Cesar [UNESP]
Souza, Andre Nunes de [UNESP]
Papa, Joao Paulo [UNESP]
Arnaud, A.
Silveira, F.
Garcia, L.
description With the consolidation of Smart Grids, a considerable amount of works can be noticed, mainly with respect to the application of several artificial intelligence techniques in order to automatically identify non-technical losses, but the problem of selecting the most representative features has not been widely discussed. In this work, we make a parallel among the problem of non-technical losses and the task of irregular consumers characterization by means of a recent meta-heuristic optimization technique called Black Hole Algorithm (BHA). The experimental setup is conducted over two private datasets provided by a Brazilian electric power company, and it shows the importance of selecting the most relevant features in the context of nontechnical losses identification, as well as the suitability of BHA to this task.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01
2018-11-26T16:48:31Z
2018-11-26T16:48:31Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv 2015 Ieee 6th Latin American Symposium On Circuits & Systems (lascas). New York: Ieee, 4 p., 2015.
2330-9954
http://hdl.handle.net/11449/161764
WOS:000380477800002
identifier_str_mv 2015 Ieee 6th Latin American Symposium On Circuits & Systems (lascas). New York: Ieee, 4 p., 2015.
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