Detalhes bibliográficos
| Ano de defesa: |
2015 |
| Autor(a) principal: |
Bulhões, Diego Bitencourt
 |
| Orientador(a): |
Cardoso, Carlos Alberto Villacorta
|
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Sergipe
|
| Programa de Pós-Graduação: |
Pós-Graduação em Engenharia Elétrica
|
| Departamento: |
Não Informado pela instituição
|
| País: |
BR
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
https://ri.ufs.br/handle/riufs/5015
|
Resumo: |
Nowadays, with the increase of electrical load demand, possible shortages of fossil fuels and strict restrictions to deployment of new hydroelectric, has seen a growing awareness for the use of environmentally clean energy source. Thus, the generation of energy through wind has been a widespread alternative in Brazil and the world. However, the accelerated expansion of wind power generation is a challenge because of the need to know the possible impacts in the operation, maintenance and interconnection with the existing energy system. The power generation forecast has been used in order to minimize this challenge, considering that the wind speed does not depend on human intervention. Therefore, in this work have been studied the potentiality of forecasting models based on artificial neural networks to forecast the wind generation in very short term horizon (a few minutes up to hours ahead) and short term horizon (a few hours up to a few days ahead). |
