Forecasting the short-term urban gas daily demand in winter based on the XGBoost algorithm

Abstract

The share of natural gas in the primary energy consumption is continuously increasing. The urban gas plays a major part in the natural gas consumption in China, and thus the accurate prediction of the short-term urban gas daily demand in winter facilitates the healthy development of the natural gas consumption and helps to maintain the secure and appropriate urban gas supply. This paper thoroughly analyzed the factors affecting the urban gas demand in winter, introduced the XGBoost algorithm for the forecast, and made accurate predictions regarding the violently-fluctuating short-term urban gas daily demand in winter. In the simulation tests, the Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the forecast made in the proposed model are 8.269 and 1.515%, respectively. Compared with other statistical algorithms and machine learning/artificial intelligence algorithms such as the multivariate regression, random forest and support vector machine, the proposed model has shown better fitting performance and higher prediction accuracy. Therefore, the XGBoost-based prediction model can provide references for the short-term urban gas daily demand forecasting.