Electricity market price forecasting by grid computing optimizing artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Niimura, T.
Data de Publicação: 2007
Outros Autores: Ozawa, K., Sakamoto, N.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/10011
Resumo: This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources.The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using real market data on twenty grid-connected PCs are reported.
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spelling Electricity market price forecasting by grid computing optimizing artificial neural networksGrid computingelectricity marketpricesforecastingneural networksThis paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources.The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using real market data on twenty grid-connected PCs are reported.Instituto Superior de Economia e GestãoRepositório da Universidade de LisboaNiimura, T.Ozawa, K.Sakamoto, N.2015-11-03T11:21:07Z20072007-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/10011engNiimura, T., K. Ozawa e N. Sakamoto (2007). "Electricity market price forecasting by grid computing optimizing artificial neural networks". Portuguese Journal of Management Studies, XII(2):133-144info: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:RCAAP2023-03-06T14:40:23Zoai:www.repository.utl.pt:10400.5/10011Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:56:33.347506Repositório 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 Electricity market price forecasting by grid computing optimizing artificial neural networks
title Electricity market price forecasting by grid computing optimizing artificial neural networks
spellingShingle Electricity market price forecasting by grid computing optimizing artificial neural networks
Niimura, T.
Grid computing
electricity market
prices
forecasting
neural networks
title_short Electricity market price forecasting by grid computing optimizing artificial neural networks
title_full Electricity market price forecasting by grid computing optimizing artificial neural networks
title_fullStr Electricity market price forecasting by grid computing optimizing artificial neural networks
title_full_unstemmed Electricity market price forecasting by grid computing optimizing artificial neural networks
title_sort Electricity market price forecasting by grid computing optimizing artificial neural networks
author Niimura, T.
author_facet Niimura, T.
Ozawa, K.
Sakamoto, N.
author_role author
author2 Ozawa, K.
Sakamoto, N.
author2_role author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Niimura, T.
Ozawa, K.
Sakamoto, N.
dc.subject.por.fl_str_mv Grid computing
electricity market
prices
forecasting
neural networks
topic Grid computing
electricity market
prices
forecasting
neural networks
description This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources.The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using real market data on twenty grid-connected PCs are reported.
publishDate 2007
dc.date.none.fl_str_mv 2007
2007-01-01T00:00:00Z
2015-11-03T11:21:07Z
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/10400.5/10011
url http://hdl.handle.net/10400.5/10011
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Niimura, T., K. Ozawa e N. Sakamoto (2007). "Electricity market price forecasting by grid computing optimizing artificial neural networks". Portuguese Journal of Management Studies, XII(2):133-144
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
publisher.none.fl_str_mv Instituto Superior de Economia e Gestão
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)
collection 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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