Abstract The effectiveness of photovoltaic power generation systems—a clean and renewable use of solar energy—depends on the amount of solar radiation. Consequently, solar radiation forecasting (especially short-term) is crucial for photovoltaic plants. In this paper, a hybrid convolutional neural network (CNN) and gate recurrent unit (GRU) method is proposed for short-term (10-min) solar radiation forecasting based on image and time-series data (i.e., radiation data at different times). The method aims to achieve…