To address the challenge of predicting distributed photovoltaic (PV) power output for improved system integration and stability, this study proposes a novel method. Given the expanding scale of distributed PV systems and their economic constraints, accurate power output prediction becomes pivotal. Conventional prediction methods are hindered by the lack of meteorological stations at most distributed PV stations. In response, we present a dynamic convolutional generative adversarial network (DC-GAN) approach coupled with satellite cloud map video frames. By extracting shading features from satellite cloud images and utilizing DC-GAN, our method forecasts short-term cloud shading effects on future radiation. We further integrate radiation data from centralized PV stations, spatial correlations of distributed PV stations, and cloud shading characteristics. This information is used to construct a predictive model combining Convolutional Neural Networks (CNN) and Long short-term memory (LSTM), enhancing prediction accuracy. Comparative experiments confirm the superiority of our proposed method over traditional approaches, substantiating its effectiveness and practicality. Our method achieves notable accuracy improvements, establishing its value in predicting distributed PV power output. This research’s findings offer a valuable contribution to the field of renewable energy integration. In numerical assessments, our method demonstrates a significant increase in prediction accuracy, outperforming conventional techniques by 3.3%. This underscores the practical relevance and efficiency of our proposed approach in enhancing distributed PV power output prediction.