PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models

Journal:
Applied Energy
Published:
DOI:
10.1016/j.apenergy.2026.127625
Affiliations:
5
Authors:
9
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Institutions Authors Share
Tianjin Renai College, China
4.833333
 0.54
Tianjin University (TJU), China
2.333333
 0.26
Huanjiang Laboratory, China
1.000000
 0.11
Zhejiang University (ZJU), China
0.500000
 0.06
State Grid Tianjin Electric Power Company, SGCC, China
0.333333
 0.04
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