Solar radiation nowcasting based on geostationary satellite images and deep learning models

Reliable solar radiation and photovoltaic power prediction is essential for the safe and stable operation of electric power systems. Cloud cover is highly related with solar radiation, but existing extrapolation-based cloud forecast methods have difficulties in capturing cloud development. Therefore, we applied two deep learning models and a physical method for solar radiation forecast. First, for the first time we applied the novel Deep Generative Model of Radar (originally developed for radar precipitation nowcasting) to predict solar radiation (named as DGMR-SO). Second, the well-known UNet model was used for comparison. Third, we developed a physical method based on cloud physical properties forecasting. An optical flow model was used to predict the five cloud properties from satellite measurements, followed by a Cloud Physical Properties algorithm to compute solar radiation from the advected cloud properties. A spatial blurring strategy was also applied to the optical flow results in order to reduce the forecast errors. Finally, the smart persistence model and the HARMONIE numerical weather prediction model forecast were utilized as benchmark methods. The forecast horizon was 0–4 h with 15 min temporal resolution. All methods have been calibrated and tested using data from the Netherlands. In general, UNet shows the lowest errors, while DGMR-SO outperforms the competitors on qualitative performance after around 45 min. The forecast accuracy of each method also depends on sky conditions. The study findings are expected to encourage the inclusion of satellite data in solar radiation nowcasting, and can provide scientific guidance for power systems and solar power plants.Our code is open-sourced at: https://github.com/Yangcherry2024/SolarRadiation-nowcast-DGMR-SO/.