This study uses short?term gas supply prediction for storage facilities as the seasonal peak?shaving volume.This approach ensured supply reliability while improving the operational efficiency and economic benefits of storage facilities,tackling the supply?demand imbalance caused by seasonal peak?valley differences.Accurate prediction of downstream users' natural gas demand could effectively reflect the required short?term gas supply from storage.Daily natural gas consumption data from a specific region during 2021-2024 was selected.Incorporating temperature variations and date types,the study comprehensively considers trend components,seasonal patterns, and holiday effects.A Prophet forecasting model suitable for predicting short?term gas supply from storage was proposed.Four performance metrics?Mean Absolute Error (r_MAE),Mean Absolute Percentage Error (r_MAP),Root Mean Square Error (r_RMS),and Coefficient of Determination (R²)?were used to comparatively evaluate the Prophet model against five other common models (including STL decomposition and VARMAX).The results show that the Prophet model achieves an r_MAE of 13.15 m3,an r_MAP of 2.71%,an r_RMS of 16.52 m3,and an R2 of 0.99 on the test set,which is significantly superior to other models.During the winter gas consumption peak period,its prediction error can be controlled within 5%.By integrating two exogenous variables?climatic conditions and date types.The Prophet model can accurately capture the seasonal and sudden fluctuation characteristics of natural gas consumption of downstream users, improve the prediction accuracy of gas storage supply,and provide key data support for peak?shaving and supply guarantee of gas storage reservoirs.