RadNet: an interpretable deep learning model for kilometer resolution solar irradiance estimation over China with Fengyun-4A satellite data

Abstract Accurate solar energy resource assessment is essential for supporting sustainable development and reducing carbon emissions in China. China’s latest-generation geostationary meteorological satellite, Fengyun-4A (FY4A), provides an opportunity to obtain high-resolution and high-accuracy global horizontal irradiance (GHI) distribution maps over China. To fully explore the potential of FY4A, this study proposes a GHI estimation method for China based on hourly ground-based GHI data and FY4A satellite data, using a new interpretable deep learning model (RadNet). The hourly GHI estimation results are compared with other statistical estimation methods and GHI databases, demonstrating that the RadNet model exhibits significantly lower error and bias. Moreover, the RadNet model exhibits good spatial and temporal generalization capability. In terms of time, the RadNet model, trained only on GHI data from 2022, demonstrates high accuracy in 2021 and 2023. In terms of space, 65% of the test stations achieve high accuracy, with RMSE < 96 W/m 2 . The annual average GHI estimation results reveal that the RadNet model outperforms the reanalysis dataset and the remote sensing product in both resolution and accuracy. The RadNet model can provide a GHI resource distribution map of China with a 4 km × 4 km horizontal resolution.