Article, 2024

Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms

APPLIED ENERGY, ISSN 0306-2619, Volume 355, 10.1016/j.apenergy.2023.122185

Contributors

Hu, Jiaxiang [1] Hu, Wei 0000-0002-9287-552X [1] Cao, Di 0000-0003-0337-7930 (Corresponding author) [1] Huang, Yuehui [2] Chen, Jianjun 0000-0003-0851-2345 [1] Li, Yahe [3] Chen, Zhe [4] Blaabjerg, Frede 0000-0001-8311-7412 [4]

Affiliations

  1. [1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu, Peoples R China
    2. [NORA names: China; Asia, East];
  2. [2] China Elect Power Res Inst, Beijing, Peoples R China
    3. [NORA names: China; Asia, East];
  3. [3] Fudan Univ, Shanghai, Peoples R China
    4. [NORA names: China; Asia, East];
  4. [4] Aalborg Univ, Dept Energy & Technol, Aalborg, Denmark
    5. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

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Keywords

Bayesian averaging regression, Newly built wind farm, Probabilistic wind power forecasting, Transformer network

Data Provider: Clarivate

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