Abstract: Abstract： Natural gas load forecasting plays a decisive role in residential life， commercial development and industrial production. And accurate short-term load forecasting can effectively quantify the uncertainty of natural gas load forecasting， which is critical for energy system operation and scheduling risk avoidance. The natural gas load affected by the seasonal effects will appear giant peak characteristics， the traditional point prediction model does not take into account the seasonal effects of natural gas， the accuracy of the prediction results is low. The SARIMA model can handle time series data with seasonal fluctuation trends and stochastic disturbances. Therefore， the SARIMA model is used to de-periodize the natural gas load as well as the first-order difference， capture the linear and seasonal features in the time series， determine the optimal parameter model based on the red pool information criterion and grid search， and proportionally divide the short-term interval forecast values. Taking the natural gas usage in Xi’an as an example， the results show that the SARIMA model used has a small error in the strong seasonal interval of the series and has a high accuracy when compared with the traditional model.

Key words: Key words： SARIMA model, seasonality, gas, interval forecast