Accurate nowcasting of cloud cover is crucial for stable solar photovoltaic (PV) electricity generation. The study develops a nowcasting algorithm that combines continuous radiance images from geostationary satellites and an advanced recurrent neural network (RNN) to predict cloud fraction at leading times of 0–4 hours at PV plants. The algorithm, named NCP_CF, is cyclically updated and tested at five PV plants and several stations in China. The results show that the NCP_CF system provides efficient, high-quality, and adaptable cloud fraction nowcasting, with an average correlation coefficient of 0.8 between predicted clear sky ratios and actual power generation within the first 2 hours of leading time. The system's performance is validated using observed cloud cover data from meteorological observation stations and all-sky imager stations, demonstrating its reliability and adaptability. The study highlights the benefits of this technique for improving the competitiveness of solar PV energy in the electricity market and enhancing grid stability.Accurate nowcasting of cloud cover is crucial for stable solar photovoltaic (PV) electricity generation. The study develops a nowcasting algorithm that combines continuous radiance images from geostationary satellites and an advanced recurrent neural network (RNN) to predict cloud fraction at leading times of 0–4 hours at PV plants. The algorithm, named NCP_CF, is cyclically updated and tested at five PV plants and several stations in China. The results show that the NCP_CF system provides efficient, high-quality, and adaptable cloud fraction nowcasting, with an average correlation coefficient of 0.8 between predicted clear sky ratios and actual power generation within the first 2 hours of leading time. The system's performance is validated using observed cloud cover data from meteorological observation stations and all-sky imager stations, demonstrating its reliability and adaptability. The study highlights the benefits of this technique for improving the competitiveness of solar PV energy in the electricity market and enhancing grid stability.