Abstract: In response to the demand for meteorological simulation and forecasting by renewable energy sources and the existing bottleneck issues, takes the generative assimilation and prediction model (Generative Assimilation and Prediction, GAP) developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences as an example, and introduces the advantages of AI models in precise forecasting, extreme event prediction, data construction in sparse observation areas, and medium- and long-term forecasting in energy meteorology.