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

@article{qinglin2026,
  title        = {{PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models}},
  author       = {Qinglin Meng et al.},
  journal      = {Applied Energy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127625},
}

TY  - JOUR
TI  - PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models
AU  - al., Qinglin Meng et
JO  - Applied Energy
PY  - 2026
ER  -

Qinglin Meng et al. (2026). PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models. *Applied Energy*. https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127625

Qinglin Meng et al.. "PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models." *Applied Energy* (2026). https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127625.

PowerMistral: A data-efficient wind power forecasting framework leveraging pre-trained large language models
Qinglin Meng et al. · Applied Energy · 2026
https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127625