Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/196091 |
Resumo: | This work presents a system based on Artificial Neural Networks and PSO (Particle Swarm Optimization) strategy, to multinodal load forecasting, i.e., forecasting in several points of the electrical network (substations, feeders, etc.). Short-term load forecasting is an important task to planning and operation of electric power systems. It is necessary precise and reliable techniques to execute the predictions. Therefore, the load forecasting uses the Adaptive Resonance Theory. To improve the precision, the PSO technique is used to choose the best parameters for the Artificial Neural Networks training. Results show that the use of this technique with a little set of training data improves the parameters of the neural network, calculated by the MAPE (mean absolute perceptual error) of the global and multinodal load forecasted. |
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Repositório Institucional da UNESP |
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Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO StrategyMultinodal Load ForecastingParticle Swarm OptimizationAdaptive Resonance TheoryArtificial Neural NetworkThis work presents a system based on Artificial Neural Networks and PSO (Particle Swarm Optimization) strategy, to multinodal load forecasting, i.e., forecasting in several points of the electrical network (substations, feeders, etc.). Short-term load forecasting is an important task to planning and operation of electric power systems. It is necessary precise and reliable techniques to execute the predictions. Therefore, the load forecasting uses the Adaptive Resonance Theory. To improve the precision, the PSO technique is used to choose the best parameters for the Artificial Neural Networks training. Results show that the use of this technique with a little set of training data improves the parameters of the neural network, calculated by the MAPE (mean absolute perceptual error) of the global and multinodal load forecasted.IFMT, Inst Educ Ciencia & Tecnol Mato Grosso, Dept Informat, Cuiaba, BrazilUniv Fed Mato Grosso, UFMT, Inst Comp, Cuiaba, BrazilUNESP Univ Estadual Paulista, Dept Engn Eletr, Ilha Solteira, BrazilUNESP Univ Estadual Paulista, Dept Engn Eletr, Ilha Solteira, BrazilIeeeIFMTUniversidade Federal de Mato Grosso do Sul (UFMS)Universidade Estadual Paulista (Unesp)Antunes, Juliana FonsecaSouza Araujo, Nelcileno Virgilio deMinussi, Carlos Roberto [UNESP]IEEE2020-12-10T19:33:04Z2020-12-10T19:33:04Z2013-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62013 Ieee Grenoble Powertech (powertech). New York: Ieee, 6 p., 2013.http://hdl.handle.net/11449/196091WOS:000387091900294Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2013 Ieee Grenoble Powertech (powertech)info:eu-repo/semantics/openAccess2021-10-23T03:13:01Zoai:repositorio.unesp.br:11449/196091Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T03:13:01Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
title |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
spellingShingle |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy Antunes, Juliana Fonseca Multinodal Load Forecasting Particle Swarm Optimization Adaptive Resonance Theory Artificial Neural Network |
title_short |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
title_full |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
title_fullStr |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
title_full_unstemmed |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
title_sort |
Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy |
author |
Antunes, Juliana Fonseca |
author_facet |
Antunes, Juliana Fonseca Souza Araujo, Nelcileno Virgilio de Minussi, Carlos Roberto [UNESP] IEEE |
author_role |
author |
author2 |
Souza Araujo, Nelcileno Virgilio de Minussi, Carlos Roberto [UNESP] IEEE |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
IFMT Universidade Federal de Mato Grosso do Sul (UFMS) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Antunes, Juliana Fonseca Souza Araujo, Nelcileno Virgilio de Minussi, Carlos Roberto [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Multinodal Load Forecasting Particle Swarm Optimization Adaptive Resonance Theory Artificial Neural Network |
topic |
Multinodal Load Forecasting Particle Swarm Optimization Adaptive Resonance Theory Artificial Neural Network |
description |
This work presents a system based on Artificial Neural Networks and PSO (Particle Swarm Optimization) strategy, to multinodal load forecasting, i.e., forecasting in several points of the electrical network (substations, feeders, etc.). Short-term load forecasting is an important task to planning and operation of electric power systems. It is necessary precise and reliable techniques to execute the predictions. Therefore, the load forecasting uses the Adaptive Resonance Theory. To improve the precision, the PSO technique is used to choose the best parameters for the Artificial Neural Networks training. Results show that the use of this technique with a little set of training data improves the parameters of the neural network, calculated by the MAPE (mean absolute perceptual error) of the global and multinodal load forecasted. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-01-01 2020-12-10T19:33:04Z 2020-12-10T19:33:04Z |
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 |
2013 Ieee Grenoble Powertech (powertech). New York: Ieee, 6 p., 2013. http://hdl.handle.net/11449/196091 WOS:000387091900294 |
identifier_str_mv |
2013 Ieee Grenoble Powertech (powertech). New York: Ieee, 6 p., 2013. WOS:000387091900294 |
url |
http://hdl.handle.net/11449/196091 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2013 Ieee Grenoble Powertech (powertech) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1799964894286577664 |