Electrical consumers data clustering through optimum-path forest

Detalhes bibliográficos
Autor(a) principal: Ramos, Caio C. O.
Data de Publicação: 2011
Outros Autores: Souza, André N., Nakamura, Rodrigo Y. M. [UNESP], Papa, João Paulo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ISAP.2011.6082217
http://hdl.handle.net/11449/73077
Resumo: Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.
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spelling Electrical consumers data clustering through optimum-path forestClusteringNon-technical LossesOptimum-Path ForestPattern RecognitionClustering techniquesData clusteringData setsElectric power companyNon-technical lossSpecific profileClustering algorithmsCrimeData processingElectric utilitiesIndustryIntelligent systemsPattern recognitionPower transmissionForestryAlgorithmsArtificial IntelligenceData ProcessingElectric Power TransmissionElectricityLossesNon-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.Department of Electrical Engineering University of São Paulo, São Paulo, São PauloDepartment of Computing UNESP - Univ. Estadual Paulista, Bauru, São PauloDepartment of Computing UNESP - Univ. Estadual Paulista, Bauru, São PauloUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Ramos, Caio C. O.Souza, André N.Nakamura, Rodrigo Y. M. [UNESP]Papa, João Paulo [UNESP]2014-05-27T11:26:20Z2014-05-27T11:26:20Z2011-12-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ISAP.2011.60822172011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011.http://hdl.handle.net/11449/7307710.1109/ISAP.2011.60822172-s2.0-836552116739039182932747194Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011info:eu-repo/semantics/openAccess2024-04-23T16:11:27Zoai:repositorio.unesp.br:11449/73077Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:53:15.041413Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Electrical consumers data clustering through optimum-path forest
title Electrical consumers data clustering through optimum-path forest
spellingShingle Electrical consumers data clustering through optimum-path forest
Ramos, Caio C. O.
Clustering
Non-technical Losses
Optimum-Path Forest
Pattern Recognition
Clustering techniques
Data clustering
Data sets
Electric power company
Non-technical loss
Specific profile
Clustering algorithms
Crime
Data processing
Electric utilities
Industry
Intelligent systems
Pattern recognition
Power transmission
Forestry
Algorithms
Artificial Intelligence
Data Processing
Electric Power Transmission
Electricity
Losses
title_short Electrical consumers data clustering through optimum-path forest
title_full Electrical consumers data clustering through optimum-path forest
title_fullStr Electrical consumers data clustering through optimum-path forest
title_full_unstemmed Electrical consumers data clustering through optimum-path forest
title_sort Electrical consumers data clustering through optimum-path forest
author Ramos, Caio C. O.
author_facet Ramos, Caio C. O.
Souza, André N.
Nakamura, Rodrigo Y. M. [UNESP]
Papa, João Paulo [UNESP]
author_role author
author2 Souza, André N.
Nakamura, Rodrigo Y. M. [UNESP]
Papa, João Paulo [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Ramos, Caio C. O.
Souza, André N.
Nakamura, Rodrigo Y. M. [UNESP]
Papa, João Paulo [UNESP]
dc.subject.por.fl_str_mv Clustering
Non-technical Losses
Optimum-Path Forest
Pattern Recognition
Clustering techniques
Data clustering
Data sets
Electric power company
Non-technical loss
Specific profile
Clustering algorithms
Crime
Data processing
Electric utilities
Industry
Intelligent systems
Pattern recognition
Power transmission
Forestry
Algorithms
Artificial Intelligence
Data Processing
Electric Power Transmission
Electricity
Losses
topic Clustering
Non-technical Losses
Optimum-Path Forest
Pattern Recognition
Clustering techniques
Data clustering
Data sets
Electric power company
Non-technical loss
Specific profile
Clustering algorithms
Crime
Data processing
Electric utilities
Industry
Intelligent systems
Pattern recognition
Power transmission
Forestry
Algorithms
Artificial Intelligence
Data Processing
Electric Power Transmission
Electricity
Losses
description Non-technical losses identification has been paramount in the last decade. Since we have datasets with hundreds of legal and illegal profiles, one may have a method to group data into subprofiles in order to minimize the search for consumers that cause great frauds. In this context, a electric power company may be interested in to go deeper a specific profile of illegal consumer. In this paper, we introduce the Optimum-Path Forest (OPF) clustering technique to this task, and we evaluate the behavior of a dataset provided by a brazilian electric power company with different values of an OPF parameter. © 2011 IEEE.
publishDate 2011
dc.date.none.fl_str_mv 2011-12-21
2014-05-27T11:26:20Z
2014-05-27T11:26:20Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ISAP.2011.6082217
2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011.
http://hdl.handle.net/11449/73077
10.1109/ISAP.2011.6082217
2-s2.0-83655211673
9039182932747194
url http://dx.doi.org/10.1109/ISAP.2011.6082217
http://hdl.handle.net/11449/73077
identifier_str_mv 2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011.
10.1109/ISAP.2011.6082217
2-s2.0-83655211673
9039182932747194
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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_ 1808129260890619904

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