PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models
Tianjin University · State Grid Corporation of China (China) · +3 more institutions
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Abstract
No abstract available for this paper.
Citation impact
7
total citations
- FWCI
- 58.46
- Percentile
- 100%
- References
- 36
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Authors
9- QMQinglin MengCorresponding
Tianjin University, State Grid Corporation of China (China), Tianjin Research Institute of Electric Science (China)
- YGYuan GaoCorresponding
Tianjin Research Institute of Electric Science (China), Zhejiang University
- YHYing He
Tianjin Research Institute of Electric Science (China)
- SHSheharyar Hussain
Tianjin Research Institute of Electric Science (China)
- JLJinghang Lu
Tianjin Research Institute of Electric Science (China)
Topics & keywords
Keywords
- Wind power
- Wind power forecasting
- Power (physics)
- Probabilistic forecasting
- Data modeling
- Weather forecasting
UN Sustainable Development Goals
- Affordable and clean energy
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