Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10216/78679| Autor(es): | J. Nuno Fidalgo Rui Camacho António F. Silva Fernando Aristides |
| Título: | Machine learning methods to predict wind intensity |
| Data de publicação: | 2008 |
| 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. |
| Assunto: | Outras ciências da engenharia e tecnologias Other engineering and technologies |
| Áreas do conhecimento: | Ciências da engenharia e tecnologias::Outras ciências da engenharia e tecnologias Engineering and technology::Other engineering and technologies |
| URI: | https://repositorio-aberto.up.pt/handle/10216/78679 |
| Fonte: | Proceedings of the WSEAS International Conference on APPLIED and THEORETICAL MECHANICS |
| Tipo de Documento: | Artigo em Livro de Atas de Conferência Internacional |
| Condições de Acesso: | openAccess |
| Licença: | https://creativecommons.org/licenses/by-nc/4.0/ |
| Aparece nas coleções: | FEUP - Artigo em Livro de Atas de Conferência Internacional |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| 100939.pdf | Machine Learning Methods to Predict Wind Intensity | 179.95 kB | Adobe PDF | ![]() Ver/Abrir |
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