Machine learning methods to predict wind intensity

Detalhes bibliográficos
Autor(a) principal: J. Nuno Fidalgo
Data de Publicação: 2008
Outros Autores: Rui Camacho, António F. Silva, Fernando Aristides
Tipo de documento: Livro
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://repositorio-aberto.up.pt/handle/10216/78679
Resumo: A decision making problem often becomes a problem of selection. In this kind of problems (decision making or fo-recasting problems) the selection of an effective set of input variables, which is usually a complex and sometimes an unmanageable process, is the main problem in real situations. The correct selection of the most important data in the assessment of a problem allows not only faster decision but the reduction of the prediction error. In this paper we use a hybrid model of a Genetic Algorithm as a heuristic tool, to select appropriate combinations of different variables that have more effect on forecasting decision making parameters, and Artificial Neural Network as a fitness function of genetic algorithm. The model was then applied to predict the intensity of the wind in the short and medium term in the central-south region of Portugal. The results proved to be excellent regardless of the forecast horizon.
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spelling Machine learning methods to predict wind intensityOutras ciências da engenharia e tecnologiasOther engineering and technologiesA decision making problem often becomes a problem of selection. In this kind of problems (decision making or fo-recasting problems) the selection of an effective set of input variables, which is usually a complex and sometimes an unmanageable process, is the main problem in real situations. The correct selection of the most important data in the assessment of a problem allows not only faster decision but the reduction of the prediction error. In this paper we use a hybrid model of a Genetic Algorithm as a heuristic tool, to select appropriate combinations of different variables that have more effect on forecasting decision making parameters, and Artificial Neural Network as a fitness function of genetic algorithm. The model was then applied to predict the intensity of the wind in the short and medium term in the central-south region of Portugal. The results proved to be excellent regardless of the forecast horizon.20082008-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/78679engJ. Nuno FidalgoRui CamachoAntónio F. SilvaFernando Aristidesinfo: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:RCAAP2023-11-29T12:39:14Zoai:repositorio-aberto.up.pt:10216/78679Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:24:12.458497Repositório 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 Machine learning methods to predict wind intensity
title Machine learning methods to predict wind intensity
spellingShingle Machine learning methods to predict wind intensity
J. Nuno Fidalgo
Outras ciências da engenharia e tecnologias
Other engineering and technologies
title_short Machine learning methods to predict wind intensity
title_full Machine learning methods to predict wind intensity
title_fullStr Machine learning methods to predict wind intensity
title_full_unstemmed Machine learning methods to predict wind intensity
title_sort Machine learning methods to predict wind intensity
author J. Nuno Fidalgo
author_facet J. Nuno Fidalgo
Rui Camacho
António F. Silva
Fernando Aristides
author_role author
author2 Rui Camacho
António F. Silva
Fernando Aristides
author2_role author
author
author
dc.contributor.author.fl_str_mv J. Nuno Fidalgo
Rui Camacho
António F. Silva
Fernando Aristides
dc.subject.por.fl_str_mv Outras ciências da engenharia e tecnologias
Other engineering and technologies
topic Outras ciências da engenharia e tecnologias
Other engineering and technologies
description A decision making problem often becomes a problem of selection. In this kind of problems (decision making or fo-recasting problems) the selection of an effective set of input variables, which is usually a complex and sometimes an unmanageable process, is the main problem in real situations. The correct selection of the most important data in the assessment of a problem allows not only faster decision but the reduction of the prediction error. In this paper we use a hybrid model of a Genetic Algorithm as a heuristic tool, to select appropriate combinations of different variables that have more effect on forecasting decision making parameters, and Artificial Neural Network as a fitness function of genetic algorithm. The model was then applied to predict the intensity of the wind in the short and medium term in the central-south region of Portugal. The results proved to be excellent regardless of the forecast horizon.
publishDate 2008
dc.date.none.fl_str_mv 2008
2008-01-01T00:00:00Z
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dc.identifier.uri.fl_str_mv https://repositorio-aberto.up.pt/handle/10216/78679
url https://repositorio-aberto.up.pt/handle/10216/78679
dc.language.iso.fl_str_mv eng
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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
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