基于静止卫星数据的太阳能发电临近预报Transformer方法

Abstract Unpredicted spatial and temporal variability of global horizontal irradiance (GHI) reaching the photovoltaic panels presents a challenge for integrating solar power into the grid stably and cost-effectively at a regional scale. Therefore, there is a recognized demand for large-scale GHI nowcasting that is both timely and accurate, an area where most existing studies fall short. This study introduces the SolarFormer model, which utilizes satellite data and incorporates a gated recurrent unit for near real-time GHI estimation. It also includes a space-time transformer to provide forecasts with a 3-h lead time at 15-min intervals, maintaining accuracy without significant degradation over extended lead times. SolarFormer requires only the selected satellite band information shared by GOES-16 and Himawari-8 as the dynamic input, enabling near-real-time application across all areas covered by these satellites. This feature makes it accessible and efficient for large-scale energy planning. We validate the forecasting result with the ground-measured GHI over seven SURFRAD stations in 2018. The model achieves an hourly prediction root-mean-square error (relative root-mean-square error) of 93.8 W/m 2 (15.0 %), 118.9 W/m 2 (19.8 %), and 129.1 W/m 2 (24.2 %) with 1–3 h lead time respectively. These results demonstrate lower root-mean-square error compared to existing hourly updated numerical weather prediction modeling, such as High-Resolution Rapid Refresh, and deep learning models, such as ConvLSTM. Moreover, the study highlights the potential of SolarFormer for extended lead-time forecasting due to its high computation and memory efficiency compared with the above-mentioned models, potentially benefiting long-term energy planning and power market bidding and clearing. However, SolarFormer exhibits accumulated bias as the predicted lead time increases and faces challenges in predicting GHI in the early morning due to the invalid visible satellite bands during the night, suggesting areas for improvement in future studies.