Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4999-2026
https://doi.org/10.5194/gmd-19-4999-2026
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

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Cited articles

Al-Dahidi, S., Madhiarasan, M., Al-Ghussain, L., Abubaker, A. M., Ahmad, A. D., Alrbai, M., Aghaei, M., Alahmer, H., Alahmer, A., Baraldi, P., and Zio, E.: Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework, Energies, https://doi.org/10.3390/en17164145, 2024. 
Alskaif, T., Dev, S., Visser, L., Hossari, M., and van Sark, W.: A systematic analysis of meteorological variables for PV output power estimation, Renew. Energ., 153, 12–22, https://doi.org/10.1016/j.renene.2020.01.150, 2020. 
Alvarenga, R., Herbaux, H., and Linguet, L.: Combination of Post-Processing Methods to Improve High-Resolution NWP Solar Irradiance Forecasts in French Guiana, Engineering Proceedings, https://doi.org/10.3390/engproc2022018027, 2022. 
Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A.: pvlib python: 2023 project update, Journal of Open Source Software, 8, 5994, https://doi.org/10.21105/joss.05994, 2023. 
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., and Antonanzas-Torres, F.: Review of photovoltaic power forecasting, Sol. Energy, 136, 78–111, https://doi.org/10.1016/j.solener.2016.06.069, 2016. 
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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.
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