Electrical load forecasting formulation by a fast neural network

Detalhes bibliográficos
Autor(a) principal: Lopes, Mara Lúcia M. [UNESP]
Data de Publicação: 2003
Outros Autores: Minussi, Carlos R. [UNESP], Lotufo, Anna Diva P. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/224313
Resumo: The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.
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spelling Electrical load forecasting formulation by a fast neural networkBackpropagationFuzzy logicLoad forecastingNeural networksShort termThe objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.Departamento de Engenharia Eletrica Universidade Estadual Paulista UNESP, Ilha Solteria, SPDepartamento de Engenharia Eletrica Universidade Estadual Paulista UNESP, Ilha Solteria, SPUniversidade Estadual Paulista (UNESP)Lopes, Mara Lúcia M. [UNESP]Minussi, Carlos R. [UNESP]Lotufo, Anna Diva P. [UNESP]2022-04-28T19:55:54Z2022-04-28T19:55:54Z2003-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article51-57International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, v. 11, n. 1, p. 51-57, 2003.1472-8915http://hdl.handle.net/11449/2243132-s2.0-0038038897Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Engineering Intelligent Systems for Electrical Engineering and Communicationsinfo:eu-repo/semantics/openAccess2022-04-28T19:55:54Zoai:repositorio.unesp.br:11449/224313Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:55:54Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Electrical load forecasting formulation by a fast neural network
title Electrical load forecasting formulation by a fast neural network
spellingShingle Electrical load forecasting formulation by a fast neural network
Lopes, Mara Lúcia M. [UNESP]
Backpropagation
Fuzzy logic
Load forecasting
Neural networks
Short term
title_short Electrical load forecasting formulation by a fast neural network
title_full Electrical load forecasting formulation by a fast neural network
title_fullStr Electrical load forecasting formulation by a fast neural network
title_full_unstemmed Electrical load forecasting formulation by a fast neural network
title_sort Electrical load forecasting formulation by a fast neural network
author Lopes, Mara Lúcia M. [UNESP]
author_facet Lopes, Mara Lúcia M. [UNESP]
Minussi, Carlos R. [UNESP]
Lotufo, Anna Diva P. [UNESP]
author_role author
author2 Minussi, Carlos R. [UNESP]
Lotufo, Anna Diva P. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Lopes, Mara Lúcia M. [UNESP]
Minussi, Carlos R. [UNESP]
Lotufo, Anna Diva P. [UNESP]
dc.subject.por.fl_str_mv Backpropagation
Fuzzy logic
Load forecasting
Neural networks
Short term
topic Backpropagation
Fuzzy logic
Load forecasting
Neural networks
Short term
description The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.
publishDate 2003
dc.date.none.fl_str_mv 2003-03-01
2022-04-28T19:55:54Z
2022-04-28T19:55:54Z
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 International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, v. 11, n. 1, p. 51-57, 2003.
1472-8915
http://hdl.handle.net/11449/224313
2-s2.0-0038038897
identifier_str_mv International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, v. 11, n. 1, p. 51-57, 2003.
1472-8915
2-s2.0-0038038897
url http://hdl.handle.net/11449/224313
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 51-57
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_ 1799964612813127680

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