Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 11
Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4999-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4999-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 11
Model description paper
 | 
12 Jun 2026
Model description paper |  | 12 Jun 2026

MIPV-NWP-PINNs V1.0: development of a multi-scale photovoltaic power forecasting framework integrating numerical weather prediction with physics-informed neural networks

Fei Zhang, Xingcai Li, Zifa Wang, Yunyun Wen, Xuyang Zhou, Zichen Wu, Zhuoran Wang, Huansheng Chen, Zhe Wang, and Xueshun Chen

Viewed

Total article views: 8,063 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,195 2,584 284 8,063 201 213 185
  • HTML: 5,195
  • PDF: 2,584
  • XML: 284
  • Total: 8,063
  • Supplement: 201
  • BibTeX: 213
  • EndNote: 185
Views and downloads (calculated since 28 Oct 2025)
Cumulative views and downloads (calculated since 28 Oct 2025)

Viewed (geographical distribution)

Total article views: 8,063 (including HTML, PDF, and XML) Thereof 8,046 with geography defined and 17 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Jun 2026
Download
Short summary
Solar power generation depends on weather conditions and photovoltaic modules, making accurate forecasts crucial for reliable grid operation. We combined weather prediction and artificial intelligence to improve the solar power prediction at different time scales for a plant. By improving sunlight predictions and incorporating physical constraints into the model, our approach reduced errors significantly. This can help integrate clean energy into power grids safely and efficiently.
Read more
Share