A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability

@article{zhijin2026,
  title        = {{A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability}},
  author       = {Zhijin Wang et al.},
  journal      = {Applied Energy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127378},
}

TY  - JOUR
TI  - A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability
AU  - al., Zhijin Wang et
JO  - Applied Energy
PY  - 2026
ER  -

Zhijin Wang et al. (2026). A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability. *Applied Energy*. https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127378

Zhijin Wang et al.. "A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability." *Applied Energy* (2026). https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127378.

A comprehensive review of deep learning for solar nowcasting: Enhancing accuracy, reliability, and interpretability
Zhijin Wang et al. · Applied Energy · 2026
https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127378