Abstract: By integrating historical data with deep generative models, a wind speed forecasting method based on deep generative models is proposed. This approach enables spatial distribution sampling and simulation for target regions, thereby more accurately guiding forecasting results and improving forecast accuracy and reliability. Experimental results from short-term hourly wind field forecasts in the Hebei region demonstrate that augmenting the U-Net forecasting model with SDEdit significantly enhances forecast accuracy and reliability compared with using the U-Net model alone. This combined model achieves lower root-mean-square error and higher anomaly correlation coefficient scores, particularly excelling in capturing small-scale information. These findings improve the accuracy and reliability of wind speed forecasts and provide valuable insights for the future development and refinement of similar forecasting models.