Journal of Resources and Ecology >
Forecasting Gas Consumption Based on a Residual AutoRegression Model and Kalman Filtering Algorithm
Received date: 20180919
Accepted date: 20190605
Online published: 20191011
Supported by
Foundation: Soft Science Research Project in Shanxi Province of China(20170410305)
Science Fund Projects in North University of China (XJJ2016037).()
Copyright
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 autoregression 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 autoregressive 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 autoregressive 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 timeseries forecasting methods, this combined model is less restrictive, performs well and the result is more credible.
ZHU Meifeng , WU Qinglong , WANG Yongqin . Forecasting Gas Consumption Based on a Residual AutoRegression Model and Kalman Filtering Algorithm[J]. Journal of Resources and Ecology, 2019 , 10(5) : 546 552 . DOI: 10.5814/j.issn.1674764X.2019.05.011
Table 1 Observated values and the fitting values of each prediction method (unit: 10^{9} m^{3}) 
Year  Observed value  Estimated value of residual autoregression  Estimated value of Kalman  Estimated value of combined forecast  Year  Observed value  Estimated value of residual autoregression  Estimated value of Kalman  Estimated value of combined forecast 

1982  12.33  12.06  13.48  12.13  2000  25.35  24.43  22.13  24.25 
1983  12.61  11.92  12.42  12.39  2001  28.37  29.17  22.39  29.05 
1984  12.84  13.22  12.58  12.79  2002  30.19  32.12  28.36  30.14 
1985  13.36  13.45  12.79  13.43  2003  35.08  32.93  28.58  32.51 
1986  14.22  14.26  13.36  14.26  2004  41.04  40.43  34.06  40.38 
1987  14.35  15.45  14.11  14.17  2005  48.21  47.29  34.62  47.23 
1988  14.84  15.03  14.29  14.95  2006  59.31  55.47  47.76  54.70 
1989  15.53  15.86  14.84  15.67  2007  72.95  69.81  53.10  69.40 
1990  15.76  16.77  14.85  15.70  2008  84.09  85.52  68.39  85.38 
1991  16.42  16.66  14.88  16.62  2009  92.60  94.45  79.96  94.15 
1992  16.41  17.74  16.39  16.39  2010  111.18  100.68  92.35  98.70 
1993  17.32  17.21  16.40  17.20  2011  137.08  127.64  92.63  126.13 
1994  17.92  18.95  16.55  18.08  2012  150.94  159.51  129.35  155.41 
1995  18.33  19.33  17.76  18.14  2013  171.92  163.04  139.83  161.39 
1996  19.10  19.64  18.32  19.22  2014  188.40  189.86  171.53  189.72 
1997  20.19  20.76  18.45  20.54  2015  194.76  202.38  175.21  200.80 
1998  20.93  22.17  20.17  20.72  2016  210.34  200.11  194.72  211.50 
1999  22.21  22.65  20.72  22.49  2017  237.30  235.40  220.27  235.21 
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