Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/78679
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dc.creatorJ. Nuno Fidalgo
dc.creatorRui Camacho
dc.creatorAntónio F. Silva
dc.creatorFernando Aristides
dc.date.accessioned2019-01-31T18:37:49Z-
dc.date.available2019-01-31T18:37:49Z-
dc.date.issued2008
dc.identifier.othersigarra:100939
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/78679-
dc.description.abstractA 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.
dc.language.isoeng
dc.relation.ispartofProceedings of the WSEAS International Conference on APPLIED and THEORETICAL MECHANICS
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectOutras ciências da engenharia e tecnologias
dc.subjectOther engineering and technologies
dc.titleMachine learning methods to predict wind intensity
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.subject.fosCiências da engenharia e tecnologias::Outras ciências da engenharia e tecnologias
dc.subject.fosEngineering and technology::Other engineering and technologies
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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