Forecasting Gas Consumption Based on a Residual Auto-Regression Model and Kalman Filtering Algorithm

Consumption of clean energy has been increasing in China. Forecasting gas consumption is important to adjusting the energy consumption structure in the future. Based on historical data of gas consumption from 1980 to 2017, this paper presents a weight method of the inverse deviation of fitted value, and a combined forecast based on a residual auto-regression model and Kalman filtering algorithm is used to forecast gas consumption. Our results show that: (1) The combination forecast is of higher precision: the relative errors of the residual auto-regressive model, the Kalman filtering algorithm and the combination model are within the range (-0.08, 0.09), (-0.09, 0.32) and (-0.03, 0.11), respectively. (2) The combination forecast is of greater stability: the variance of relative error of the residual auto-regressive model, the Kalman filtering algorithm and the combination model are 0.002, 0.007 and 0.001, respectively. (3) Provided that other conditions are invariant, the predicted value of gas consumption in 2018 is 241.81×10 ⁹ m ³. Compared to other time-series forecasting methods, this combined model is less restrictive, performs well and the result is more credible.