Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UNIOESTE |
Texto Completo: | http://tede.unioeste.br/handle/tede/5712 |
Resumo: | The present work analyzed the comparison between feedforwards, recurrent, convolutional and bidirectional artificial neural networks based on different layers architectures as a predictive tool for short-term load forecasting. These forecasting models can serve as a support instrument related to the decision making of companies in the energy sector, as the demand for energy is requested one day before its transmission in much of the world. The code of the artificial neural networks was programmed in Python using the Keras package. Forecasts for all networks have been performed 10 times until an acceptable statistical sample is reached so that future values demand for energy are as close as possible to reality. The best forecasting model was the BiGRU_CNN network where the average errors attributed to its predictions in a 24-hour horizon was 3.42% for the MAPE error, 100.75 MW for the MAE accuracy metric and 122.2 MW for the RMSE error. |
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Franco, Edgar Manuel Carrenohttp://lattes.cnpq.br/4430719667450640Machado, Renato Bobsinhttp://lattes.cnpq.br/8407723021436270Müller, Marcos Ricardohttp://lattes.cnpq.br/6275900986006185http://lattes.cnpq.br/4034781064552418Soares, Lucas Duarte2021-12-08T17:33:00Z2021-09-03Soares, Lucas Duarte. Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo. 2021. 118 f. Dissertação (Programa de Pós-Graduação em Engenharia Elétrica e Computação) - Universidade Estadual do Oeste do Paraná, Foz do Iguaçu,2021 .http://tede.unioeste.br/handle/tede/5712The present work analyzed the comparison between feedforwards, recurrent, convolutional and bidirectional artificial neural networks based on different layers architectures as a predictive tool for short-term load forecasting. These forecasting models can serve as a support instrument related to the decision making of companies in the energy sector, as the demand for energy is requested one day before its transmission in much of the world. The code of the artificial neural networks was programmed in Python using the Keras package. Forecasts for all networks have been performed 10 times until an acceptable statistical sample is reached so that future values demand for energy are as close as possible to reality. The best forecasting model was the BiGRU_CNN network where the average errors attributed to its predictions in a 24-hour horizon was 3.42% for the MAPE error, 100.75 MW for the MAE accuracy metric and 122.2 MW for the RMSE error.O presente trabalho analisou o comparativo entre redes neurais artificiais feedforwards, recorrentes, convolucionais e bidirecionais baseadas em camadas de diferentes arquiteturas como ferramenta preditiva de demanda de energia elétrica de curto prazo. Esses modelos de previsões podem servir como instrumento de apoio relacionados à tomada de decisão de empresas do setor energético, em virtude da demanda de energia ser estabelecida um dia antes de sua transmissão em boa parte do mundo. O código das redes neurais artificiais foi programado em Python fazendo uso do pacote Keras. As previsões de todas as redes foram realizadas 10 vezes até se chegar a uma amostra estatística aceitável para que valores futuros de demanda de energia sejam os mais próximos possíveis da realidade. O melhor modelo de previsão foi o da rede BiGRU_CNN, onde os erros médios atribuídos as suas previsões em um horizonte de 24 horas foram de 3,42% para o erro MAPE, 100,75 MW para a métrica de acurácia MAE e 122,2 MW para o erro RMSE.Submitted by Katia Abreu (katia.abreu@unioeste.br) on 2021-12-08T17:33:00Z No. of bitstreams: 2 Lucas_Duarte_Soares_2021.pdf: 3825185 bytes, checksum: a3ffeeebc9ec8e5084415b85be54af4f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Made available in DSpace on 2021-12-08T17:33:00Z (GMT). No. of bitstreams: 2 Lucas_Duarte_Soares_2021.pdf: 3825185 bytes, checksum: a3ffeeebc9ec8e5084415b85be54af4f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2021-09-03Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfpor8774263440366006536500Universidade Estadual do Oeste do ParanáFoz do IguaçuPrograma de Pós-Graduação em Engenharia Elétrica e ComputaçãoUNIOESTEBrasilCentro de Engenharias e Ciências Exatashttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessInteligência artificialAprendizado de máquinhaRedes neurais recorrentesSéries temporaisArtificial intelligence,Machine learning,Recurrent neural network,Time seriesENGENHARIA ELETRICA::TELECOMUNICACOESRedes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazoBIGRU_CNN artificial neural networks applied to forecasting of short-term electricity demandinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis-1040084669565072649600600600600-7734402124082146922-74271990478720904792075167498588264571reponame:Biblioteca Digital de Teses e Dissertações do UNIOESTEinstname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEORIGINALLucas_Duarte_Soares_2021.pdfLucas_Duarte_Soares_2021.pdfapplication/pdf3825185http://tede.unioeste.br:8080/tede/bitstream/tede/5712/5/Lucas_Duarte_Soares_2021.pdfa3ffeeebc9ec8e5084415b85be54af4fMD55CC-LICENSElicense_urllicense_urltext/plain; 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dc.title.por.fl_str_mv |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
dc.title.alternative.eng.fl_str_mv |
BIGRU_CNN artificial neural networks applied to forecasting of short-term electricity demand |
title |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
spellingShingle |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo Soares, Lucas Duarte Inteligência artificial Aprendizado de máquinha Redes neurais recorrentes Séries temporais Artificial intelligence, Machine learning, Recurrent neural network, Time series ENGENHARIA ELETRICA::TELECOMUNICACOES |
title_short |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
title_full |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
title_fullStr |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
title_full_unstemmed |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
title_sort |
Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo |
author |
Soares, Lucas Duarte |
author_facet |
Soares, Lucas Duarte |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Franco, Edgar Manuel Carreno |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4430719667450640 |
dc.contributor.referee1.fl_str_mv |
Machado, Renato Bobsin |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/8407723021436270 |
dc.contributor.referee2.fl_str_mv |
Müller, Marcos Ricardo |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/6275900986006185 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4034781064552418 |
dc.contributor.author.fl_str_mv |
Soares, Lucas Duarte |
contributor_str_mv |
Franco, Edgar Manuel Carreno Machado, Renato Bobsin Müller, Marcos Ricardo |
dc.subject.por.fl_str_mv |
Inteligência artificial Aprendizado de máquinha Redes neurais recorrentes Séries temporais |
topic |
Inteligência artificial Aprendizado de máquinha Redes neurais recorrentes Séries temporais Artificial intelligence, Machine learning, Recurrent neural network, Time series ENGENHARIA ELETRICA::TELECOMUNICACOES |
dc.subject.eng.fl_str_mv |
Artificial intelligence, Machine learning, Recurrent neural network, Time series |
dc.subject.cnpq.fl_str_mv |
ENGENHARIA ELETRICA::TELECOMUNICACOES |
description |
The present work analyzed the comparison between feedforwards, recurrent, convolutional and bidirectional artificial neural networks based on different layers architectures as a predictive tool for short-term load forecasting. These forecasting models can serve as a support instrument related to the decision making of companies in the energy sector, as the demand for energy is requested one day before its transmission in much of the world. The code of the artificial neural networks was programmed in Python using the Keras package. Forecasts for all networks have been performed 10 times until an acceptable statistical sample is reached so that future values demand for energy are as close as possible to reality. The best forecasting model was the BiGRU_CNN network where the average errors attributed to its predictions in a 24-hour horizon was 3.42% for the MAPE error, 100.75 MW for the MAE accuracy metric and 122.2 MW for the RMSE error. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-12-08T17:33:00Z |
dc.date.issued.fl_str_mv |
2021-09-03 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Soares, Lucas Duarte. Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo. 2021. 118 f. Dissertação (Programa de Pós-Graduação em Engenharia Elétrica e Computação) - Universidade Estadual do Oeste do Paraná, Foz do Iguaçu,2021 . |
dc.identifier.uri.fl_str_mv |
http://tede.unioeste.br/handle/tede/5712 |
identifier_str_mv |
Soares, Lucas Duarte. Redes neurais artificiais BIGRU_CNN aplicadas à previsão de demanda de energia elétrica de curto prazo. 2021. 118 f. Dissertação (Programa de Pós-Graduação em Engenharia Elétrica e Computação) - Universidade Estadual do Oeste do Paraná, Foz do Iguaçu,2021 . |
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http://tede.unioeste.br/handle/tede/5712 |
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openAccess |
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Universidade Estadual do Oeste do Paraná Foz do Iguaçu |
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Programa de Pós-Graduação em Engenharia Elétrica e Computação |
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UNIOESTE |
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Brasil |
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Centro de Engenharias e Ciências Exatas |
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Universidade Estadual do Oeste do Paraná Foz do Iguaçu |
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