Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2018 |
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/10400.6/8221 |
Resumo: | Short-term load forecasting plays a preponderant role in the daily basis system's operation and planning. The state- of-the-art comprises a far-reaching set of methodologies, which are traditionally based on time-series analysis and multilayer neural networks. In particular, the existence of countless neural network architectures has highlighted its ability to cope with 'hard' nonlinear approximation tasks, thus making them appropriate to perform load forecasts. Following this successful path, long short-term memory networks were employed in an optimized arrangement as forecasters, this type of recurrent neural networks has received in recent years a renewed interest for machine learning tasks. Firstly, a preprocessing stage takes place, where through the selection of similar days and correlation analysis, meaningful statistics and characteristics are extracted from the load time-series, to assemble the proper training sets. Then, Bat Algorithm is used to excel the long short-term memory network functioning, by fine-tuning its size and its learning hyperparameters. Numerical testing conducted on the Portuguese load time-series reveals promising forecasting results in an overall assessment, when compared with other state-of-the-art methods. |
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Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat AlgorithmBat algorithmEvolutionary long short term memory networksGradient descent optimizationHyperparameters optimizationShort-term load forecastingSimilar day selectionShort-term load forecasting plays a preponderant role in the daily basis system's operation and planning. The state- of-the-art comprises a far-reaching set of methodologies, which are traditionally based on time-series analysis and multilayer neural networks. In particular, the existence of countless neural network architectures has highlighted its ability to cope with 'hard' nonlinear approximation tasks, thus making them appropriate to perform load forecasts. Following this successful path, long short-term memory networks were employed in an optimized arrangement as forecasters, this type of recurrent neural networks has received in recent years a renewed interest for machine learning tasks. Firstly, a preprocessing stage takes place, where through the selection of similar days and correlation analysis, meaningful statistics and characteristics are extracted from the load time-series, to assemble the proper training sets. Then, Bat Algorithm is used to excel the long short-term memory network functioning, by fine-tuning its size and its learning hyperparameters. Numerical testing conducted on the Portuguese load time-series reveals promising forecasting results in an overall assessment, when compared with other state-of-the-art methods.Institute of Electrical and Electronics EngineersuBibliorumBento, P.M.R.Pombo, José Álvaro NunesMariano, S.Calado, M. Do Rosário2020-01-10T17:05:41Z2018-09-252018-09-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/8221eng10.1109/IS.2018.8710498metadata 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:RCAAP2023-12-15T09:48:04Zoai:ubibliorum.ubi.pt:10400.6/8221Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:48:36.436821Repositório 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 |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
title |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
spellingShingle |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm Bento, P.M.R. Bat algorithm Evolutionary long short term memory networks Gradient descent optimization Hyperparameters optimization Short-term load forecasting Similar day selection |
title_short |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
title_full |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
title_fullStr |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
title_full_unstemmed |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
title_sort |
Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm |
author |
Bento, P.M.R. |
author_facet |
Bento, P.M.R. Pombo, José Álvaro Nunes Mariano, S. Calado, M. Do Rosário |
author_role |
author |
author2 |
Pombo, José Álvaro Nunes Mariano, S. Calado, M. Do Rosário |
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 Mariano, S. Calado, M. Do Rosário |
dc.subject.por.fl_str_mv |
Bat algorithm Evolutionary long short term memory networks Gradient descent optimization Hyperparameters optimization Short-term load forecasting Similar day selection |
topic |
Bat algorithm Evolutionary long short term memory networks Gradient descent optimization Hyperparameters optimization Short-term load forecasting Similar day selection |
description |
Short-term load forecasting plays a preponderant role in the daily basis system's operation and planning. The state- of-the-art comprises a far-reaching set of methodologies, which are traditionally based on time-series analysis and multilayer neural networks. In particular, the existence of countless neural network architectures has highlighted its ability to cope with 'hard' nonlinear approximation tasks, thus making them appropriate to perform load forecasts. Following this successful path, long short-term memory networks were employed in an optimized arrangement as forecasters, this type of recurrent neural networks has received in recent years a renewed interest for machine learning tasks. Firstly, a preprocessing stage takes place, where through the selection of similar days and correlation analysis, meaningful statistics and characteristics are extracted from the load time-series, to assemble the proper training sets. Then, Bat Algorithm is used to excel the long short-term memory network functioning, by fine-tuning its size and its learning hyperparameters. Numerical testing conducted on the Portuguese load time-series reveals promising forecasting results in an overall assessment, when compared with other state-of-the-art methods. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-25 2018-09-25T00:00:00Z 2020-01-10T17:05:41Z |
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/10400.6/8221 |
url |
http://hdl.handle.net/10400.6/8221 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/IS.2018.8710498 |
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 |
Institute of Electrical and Electronics Engineers |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136379827912704 |