A hierarchical self-organizing map model in short-termload forecasting.

Detalhes bibliográficos
Autor(a) principal: Carpinteiro, Otávio Augusto Salgado
Data de Publicação: 2004
Outros Autores: Reis, Agnaldo José da Rocha
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UFOP
Texto Completo: http://www.repositorio.ufop.br/handle/123456789/1190
Resumo: This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets|one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them
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spelling A hierarchical self-organizing map model in short-termload forecasting.Short-term load forecastingSelf-organizing mapNeural networkThis paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets|one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them2012-07-24T14:53:14Z2012-07-24T14:53:14Z2004info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectapplication/pdfCARPINTEIRO, O. A. S.; REIS, A. J. da R. A hierarchical self-organizing map model in short-termload forecasting. In: Congresso Brasileiro de Automática, 15., 2004. Gramado. Anais... XV Congresso Brasileiro de Automática, 2004. p.1-6. Disponível em: <http://www.lti.pcs.usp.br/robotics/grva/publicacoes/outras/cba2004-cd-rom/cba2004/pdf/548.pdf>. Acesso em: 23 jul. 2012.http://www.repositorio.ufop.br/handle/123456789/1190Carpinteiro, Otávio Augusto SalgadoReis, Agnaldo José da Rochaengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOPinfo:eu-repo/semantics/openAccess2016-10-27T13:54:35Zoai:repositorio.ufop.br:123456789/1190Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332016-10-27T13:54:35Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false
dc.title.none.fl_str_mv A hierarchical self-organizing map model in short-termload forecasting.
title A hierarchical self-organizing map model in short-termload forecasting.
spellingShingle A hierarchical self-organizing map model in short-termload forecasting.
Carpinteiro, Otávio Augusto Salgado
Short-term load forecasting
Self-organizing map
Neural network
title_short A hierarchical self-organizing map model in short-termload forecasting.
title_full A hierarchical self-organizing map model in short-termload forecasting.
title_fullStr A hierarchical self-organizing map model in short-termload forecasting.
title_full_unstemmed A hierarchical self-organizing map model in short-termload forecasting.
title_sort A hierarchical self-organizing map model in short-termload forecasting.
author Carpinteiro, Otávio Augusto Salgado
author_facet Carpinteiro, Otávio Augusto Salgado
Reis, Agnaldo José da Rocha
author_role author
author2 Reis, Agnaldo José da Rocha
author2_role author
dc.contributor.author.fl_str_mv Carpinteiro, Otávio Augusto Salgado
Reis, Agnaldo José da Rocha
dc.subject.por.fl_str_mv Short-term load forecasting
Self-organizing map
Neural network
topic Short-term load forecasting
Self-organizing map
Neural network
description This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets|one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was required to predict once every hour the electric load during the next 24 hours. The paper presents the results, and evaluates them
publishDate 2004
dc.date.none.fl_str_mv 2004
2012-07-24T14:53:14Z
2012-07-24T14:53:14Z
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 CARPINTEIRO, O. A. S.; REIS, A. J. da R. A hierarchical self-organizing map model in short-termload forecasting. In: Congresso Brasileiro de Automática, 15., 2004. Gramado. Anais... XV Congresso Brasileiro de Automática, 2004. p.1-6. Disponível em: <http://www.lti.pcs.usp.br/robotics/grva/publicacoes/outras/cba2004-cd-rom/cba2004/pdf/548.pdf>. Acesso em: 23 jul. 2012.
http://www.repositorio.ufop.br/handle/123456789/1190
identifier_str_mv CARPINTEIRO, O. A. S.; REIS, A. J. da R. A hierarchical self-organizing map model in short-termload forecasting. In: Congresso Brasileiro de Automática, 15., 2004. Gramado. Anais... XV Congresso Brasileiro de Automática, 2004. p.1-6. Disponível em: <http://www.lti.pcs.usp.br/robotics/grva/publicacoes/outras/cba2004-cd-rom/cba2004/pdf/548.pdf>. Acesso em: 23 jul. 2012.
url http://www.repositorio.ufop.br/handle/123456789/1190
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFOP
instname:Universidade Federal de Ouro Preto (UFOP)
instacron:UFOP
instname_str Universidade Federal de Ouro Preto (UFOP)
instacron_str UFOP
institution UFOP
reponame_str Repositório Institucional da UFOP
collection Repositório Institucional da UFOP
repository.name.fl_str_mv Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)
repository.mail.fl_str_mv repositorio@ufop.edu.br
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