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

作     者：ZHU Meifeng WU Qinglong WANG Yongqin 朱美峰;吴青龙;王永琴

作者机构：School of Economics and ManagementNorth University of ChinaTaiyuan 030051China 

出 版 物：《Journal of Resources and Ecology》 (资源与生态学报（英文版）)

年 卷 期：2019年第10卷第5期

页      面：546-552页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 08[工学] 081404[工学-供热、供燃气、通风及空调工程] 0814[工学-土木工程] 

基　　金：Soft Science Research Project in Shanxi Province of China(2017041030-5) Science Fund Projects in North University of China(XJJ2016037) 

主　　题：residual auto-regressive model Kalman filtering algorithm inverse fitting value deviation method combined forecast 

摘      要：Consumption of clean energy has been increasing in *** gas consumption is important to adjusting the energy consumption structure in the *** 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 *** 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^*** to other time-series forecasting methods,this combined model is less restrictive,performs well and the result is more credible.