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An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting.
Li, Guomin; Yu, Leyi; Zhang, Ying; Sun, Peng; Li, Ruixuan; Zhang, Yagang; Li, Gengyin; Wang, Pengfei.
Afiliação
  • Li G; State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.
  • Yu L; State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.
  • Zhang Y; Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Box 205, Baoding, 071003, Hebei, China.
  • Sun P; State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.
  • Li R; Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Box 205, Baoding, 071003, Hebei, China.
  • Zhang Y; State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.
  • Li G; Hebei Key Laboratory of Physics and Energy Technology, North China Electric Power University, Box 205, Baoding, 071003, Hebei, China.
  • Wang P; State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.
Environ Sci Pollut Res Int ; 30(14): 41937-41953, 2023 Mar.
Article em En | MEDLINE | ID: mdl-36640232
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Energia Renovável Tipo de estudo: Prognostic_studies Idioma: En Revista: Environ Sci Pollut Res Int Assunto da revista: SAUDE AMBIENTAL / TOXICOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Alemanha
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Energia Renovável Tipo de estudo: Prognostic_studies Idioma: En Revista: Environ Sci Pollut Res Int Assunto da revista: SAUDE AMBIENTAL / TOXICOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Alemanha