A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training

Detalhes bibliográficos
Autor(a) principal: Amorim, Aline J. [UNESP]
Data de Publicação: 2020
Outros Autores: Abreu, Thays A. [UNESP], Tonelli-Neto, Mauro S. [UNESP], Minussi, Carlos R. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.epsr.2019.106096
http://hdl.handle.net/11449/198136
Resumo: A multinodal intelligent predictive method for electrical power systems has been developed. Knowing the electrical load accurately and in advance is essential for conducting studies in regard to the system operations, and to create strategies that improve the quality of the energy-supply for commercial, industrial, and residential consumers. The proposed method employs a supervised Fuzzy-ARTMAP neural network, using the new concept of reverse training, to forecast the global demand and load of several nodes of an electric network (multinodal load forecasting) up to 24 h ahead. To evaluate and test the proposed system, an application is presented that considers real historical data from a company in the electric sector. Results show that the reverse training reduces the error of the neural network, making the forecast more accurate, reliable, and very fast.
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spelling A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse trainingAdaptive resonance theoryArtificial neural networksElectrical power systemsMultinodal load forecastingA multinodal intelligent predictive method for electrical power systems has been developed. Knowing the electrical load accurately and in advance is essential for conducting studies in regard to the system operations, and to create strategies that improve the quality of the energy-supply for commercial, industrial, and residential consumers. The proposed method employs a supervised Fuzzy-ARTMAP neural network, using the new concept of reverse training, to forecast the global demand and load of several nodes of an electric network (multinodal load forecasting) up to 24 h ahead. To evaluate and test the proposed system, an application is presented that considers real historical data from a company in the electric sector. Results show that the reverse training reduces the error of the neural network, making the forecast more accurate, reliable, and very fast.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Electrical Engineering Department UNESP – São Paulo State University, Av. Brasil 56, PO Box 31Electrical Engineering Department UNESP – São Paulo State University, Av. Brasil 56, PO Box 31Universidade Estadual Paulista (Unesp)Amorim, Aline J. [UNESP]Abreu, Thays A. [UNESP]Tonelli-Neto, Mauro S. [UNESP]Minussi, Carlos R. [UNESP]2020-12-12T01:00:10Z2020-12-12T01:00:10Z2020-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.epsr.2019.106096Electric Power Systems Research, v. 179.0378-7796http://hdl.handle.net/11449/19813610.1016/j.epsr.2019.1060962-s2.0-85074928091Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengElectric Power Systems Researchinfo:eu-repo/semantics/openAccess2021-10-23T09:05:58Zoai:repositorio.unesp.br:11449/198136Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:58:04.959143Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
title A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
spellingShingle A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
Amorim, Aline J. [UNESP]
Adaptive resonance theory
Artificial neural networks
Electrical power systems
Multinodal load forecasting
title_short A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
title_full A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
title_fullStr A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
title_full_unstemmed A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
title_sort A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
author Amorim, Aline J. [UNESP]
author_facet Amorim, Aline J. [UNESP]
Abreu, Thays A. [UNESP]
Tonelli-Neto, Mauro S. [UNESP]
Minussi, Carlos R. [UNESP]
author_role author
author2 Abreu, Thays A. [UNESP]
Tonelli-Neto, Mauro S. [UNESP]
Minussi, Carlos R. [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Amorim, Aline J. [UNESP]
Abreu, Thays A. [UNESP]
Tonelli-Neto, Mauro S. [UNESP]
Minussi, Carlos R. [UNESP]
dc.subject.por.fl_str_mv Adaptive resonance theory
Artificial neural networks
Electrical power systems
Multinodal load forecasting
topic Adaptive resonance theory
Artificial neural networks
Electrical power systems
Multinodal load forecasting
description A multinodal intelligent predictive method for electrical power systems has been developed. Knowing the electrical load accurately and in advance is essential for conducting studies in regard to the system operations, and to create strategies that improve the quality of the energy-supply for commercial, industrial, and residential consumers. The proposed method employs a supervised Fuzzy-ARTMAP neural network, using the new concept of reverse training, to forecast the global demand and load of several nodes of an electric network (multinodal load forecasting) up to 24 h ahead. To evaluate and test the proposed system, an application is presented that considers real historical data from a company in the electric sector. Results show that the reverse training reduces the error of the neural network, making the forecast more accurate, reliable, and very fast.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T01:00:10Z
2020-12-12T01:00:10Z
2020-02-01
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 http://dx.doi.org/10.1016/j.epsr.2019.106096
Electric Power Systems Research, v. 179.
0378-7796
http://hdl.handle.net/11449/198136
10.1016/j.epsr.2019.106096
2-s2.0-85074928091
url http://dx.doi.org/10.1016/j.epsr.2019.106096
http://hdl.handle.net/11449/198136
identifier_str_mv Electric Power Systems Research, v. 179.
0378-7796
10.1016/j.epsr.2019.106096
2-s2.0-85074928091
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Electric Power Systems Research
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_ 1808129567907381248

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