A fast electric load forecasting using neural networks

Detalhes bibliográficos
Autor(a) principal: Lopes, Mara Lúcia M. [UNESP]
Data de Publicação: 2000
Outros Autores: Minussi, Carlos R. [UNESP], Lotufo, Anna Diva P. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/MWSCAS.2000.952840
http://hdl.handle.net/11449/66342
Resumo: The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.
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spelling A fast electric load forecasting using neural networksBackpropagationFuzzy controlFuzzy setsGradient methodsKalman filteringNeural networksRegression analysisBinary systemsLinear regressionElectric load forecastingThe objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.Universidade Estadual Paulista-UNESP Departamento de Engenharia Eletrica, Campus de Ilha Solteira, Ilha Solteira - SPUniversidade Estadual Paulista-UNESP Departamento de Engenharia Eletrica, Campus de Ilha Solteira, Ilha Solteira - SPUniversidade Estadual Paulista (Unesp)Lopes, Mara Lúcia M. [UNESP]Minussi, Carlos R. [UNESP]Lotufo, Anna Diva P. [UNESP]2014-05-27T11:19:59Z2014-05-27T11:19:59Z2000-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject646-649http://dx.doi.org/10.1109/MWSCAS.2000.952840Midwest Symposium on Circuits and Systems, v. 2, p. 646-649.http://hdl.handle.net/11449/6634210.1109/MWSCAS.2000.952840WOS:0001720993001502-s2.0-00344634985434299135943285Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengMidwest Symposium on Circuits and Systemsinfo:eu-repo/semantics/openAccess2021-10-23T21:41:29Zoai:repositorio.unesp.br:11449/66342Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:41:29Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A fast electric load forecasting using neural networks
title A fast electric load forecasting using neural networks
spellingShingle A fast electric load forecasting using neural networks
Lopes, Mara Lúcia M. [UNESP]
Backpropagation
Fuzzy control
Fuzzy sets
Gradient methods
Kalman filtering
Neural networks
Regression analysis
Binary systems
Linear regression
Electric load forecasting
title_short A fast electric load forecasting using neural networks
title_full A fast electric load forecasting using neural networks
title_fullStr A fast electric load forecasting using neural networks
title_full_unstemmed A fast electric load forecasting using neural networks
title_sort A fast electric load forecasting using neural networks
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 control
Fuzzy sets
Gradient methods
Kalman filtering
Neural networks
Regression analysis
Binary systems
Linear regression
Electric load forecasting
topic Backpropagation
Fuzzy control
Fuzzy sets
Gradient methods
Kalman filtering
Neural networks
Regression analysis
Binary systems
Linear regression
Electric load forecasting
description The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.
publishDate 2000
dc.date.none.fl_str_mv 2000-12-01
2014-05-27T11:19:59Z
2014-05-27T11:19:59Z
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/MWSCAS.2000.952840
Midwest Symposium on Circuits and Systems, v. 2, p. 646-649.
http://hdl.handle.net/11449/66342
10.1109/MWSCAS.2000.952840
WOS:000172099300150
2-s2.0-0034463498
5434299135943285
url http://dx.doi.org/10.1109/MWSCAS.2000.952840
http://hdl.handle.net/11449/66342
identifier_str_mv Midwest Symposium on Circuits and Systems, v. 2, p. 646-649.
10.1109/MWSCAS.2000.952840
WOS:000172099300150
2-s2.0-0034463498
5434299135943285
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Midwest Symposium on Circuits and Systems
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 646-649
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_ 1799965503846875136

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