Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/7142 |
Resumo: | Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology. |
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Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecastingArtificial neural networksBat algorithmFeatures extractionImproved data selectionShort-term load forecastWavelet transformShort-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology.ElsevieruBibliorumBento, P. M. R.Pombo, José Álvaro NunesCalado, M. Do RosárioMariano, S.2019-07-23T09:32:38Z2019-09-172019-09-17T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/7142eng0925231210.1016/j.neucom.2019.05.030metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:46:16Zoai:ubibliorum.ubi.pt:10400.6/7142Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:47:43.266557Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
title |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
spellingShingle |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting Bento, P. M. R. Artificial neural networks Bat algorithm Features extraction Improved data selection Short-term load forecast Wavelet transform |
title_short |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
title_full |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
title_fullStr |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
title_full_unstemmed |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
title_sort |
Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting |
author |
Bento, P. M. R. |
author_facet |
Bento, P. M. R. Pombo, José Álvaro Nunes Calado, M. Do Rosário Mariano, S. |
author_role |
author |
author2 |
Pombo, José Álvaro Nunes Calado, M. Do Rosário Mariano, S. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
uBibliorum |
dc.contributor.author.fl_str_mv |
Bento, P. M. R. Pombo, José Álvaro Nunes Calado, M. Do Rosário Mariano, S. |
dc.subject.por.fl_str_mv |
Artificial neural networks Bat algorithm Features extraction Improved data selection Short-term load forecast Wavelet transform |
topic |
Artificial neural networks Bat algorithm Features extraction Improved data selection Short-term load forecast Wavelet transform |
description |
Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-23T09:32:38Z 2019-09-17 2019-09-17T00:00:00Z |
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://hdl.handle.net/10400.6/7142 |
url |
http://hdl.handle.net/10400.6/7142 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
09252312 10.1016/j.neucom.2019.05.030 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136372083130368 |