JADE-Based Feature Selection for Non-technical Losses Detection
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Capítulo de livro |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-32040-9_16 http://hdl.handle.net/11449/201219 |
Resumo: | Nowadays, 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. |
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Repositório Institucional da UNESP |
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JADE-Based Feature Selection for Non-technical Losses DetectionAdaptive differential evolutionEnergy theft detectionFeature selectionJADENowadays, 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)School of Sciences UNESP - São Paulo State UniversityDepartment of Computing UFSCar - Federal University of São CarlosSchool of Sciences UNESP - São Paulo State UniversityFAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 2016/19403-6FAPESP: 2017/02286-0CNPq: 307066/2017-7CNPq: 427968/2018-6Universidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Pereira, Clayton Reginaldo [UNESP]Passos, Leandro Aparecido [UNESP]Rodrigues, Douglasde Souza, André Nunes [UNESP]Papa, João P. [UNESP]2020-12-12T02:27:07Z2020-12-12T02:27:07Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart141-156http://dx.doi.org/10.1007/978-3-030-32040-9_16Lecture Notes in Computational Vision and Biomechanics, v. 34, p. 141-156.2212-94132212-9391http://hdl.handle.net/11449/20121910.1007/978-3-030-32040-9_162-s2.0-85073170103Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computational Vision and Biomechanicsinfo:eu-repo/semantics/openAccess2024-04-23T16:11:01Zoai:repositorio.unesp.br:11449/201219Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:01Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
JADE-Based Feature Selection for Non-technical Losses Detection |
title |
JADE-Based Feature Selection for Non-technical Losses Detection |
spellingShingle |
JADE-Based Feature Selection for Non-technical Losses Detection Pereira, Clayton Reginaldo [UNESP] Adaptive differential evolution Energy theft detection Feature selection JADE |
title_short |
JADE-Based Feature Selection for Non-technical Losses Detection |
title_full |
JADE-Based Feature Selection for Non-technical Losses Detection |
title_fullStr |
JADE-Based Feature Selection for Non-technical Losses Detection |
title_full_unstemmed |
JADE-Based Feature Selection for Non-technical Losses Detection |
title_sort |
JADE-Based Feature Selection for Non-technical Losses Detection |
author |
Pereira, Clayton Reginaldo [UNESP] |
author_facet |
Pereira, Clayton Reginaldo [UNESP] Passos, Leandro Aparecido [UNESP] Rodrigues, Douglas de Souza, André Nunes [UNESP] Papa, João P. [UNESP] |
author_role |
author |
author2 |
Passos, Leandro Aparecido [UNESP] Rodrigues, Douglas de Souza, André Nunes [UNESP] Papa, João P. [UNESP] |
author2_role |
author 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 |
Pereira, Clayton Reginaldo [UNESP] Passos, Leandro Aparecido [UNESP] Rodrigues, Douglas de Souza, André Nunes [UNESP] Papa, João P. [UNESP] |
dc.subject.por.fl_str_mv |
Adaptive differential evolution Energy theft detection Feature selection JADE |
topic |
Adaptive differential evolution Energy theft detection Feature selection JADE |
description |
Nowadays, 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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T02:27:07Z 2020-12-12T02:27:07Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/bookPart |
format |
bookPart |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-32040-9_16 Lecture Notes in Computational Vision and Biomechanics, v. 34, p. 141-156. 2212-9413 2212-9391 http://hdl.handle.net/11449/201219 10.1007/978-3-030-32040-9_16 2-s2.0-85073170103 |
url |
http://dx.doi.org/10.1007/978-3-030-32040-9_16 http://hdl.handle.net/11449/201219 |
identifier_str_mv |
Lecture Notes in Computational Vision and Biomechanics, v. 34, p. 141-156. 2212-9413 2212-9391 10.1007/978-3-030-32040-9_16 2-s2.0-85073170103 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computational Vision and Biomechanics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
141-156 |
dc.source.none.fl_str_mv |
Scopus 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_ |
1797790044934635520 |