Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model

Abstract

China's primary energy consumption increases rapidly, which is highly related to China's sustainable development and has great impact on global energy market. Two univariate models, ARIMA (the autoregressive integrated moving average) model and GM(1,1) model, are used to forecast China's primary energy consumption. The results of the two models are in line with requirements. Through comparing, it is found that the fitted values of ARIMA model respond less to the fluctuations because they are bounded by its long-term trend while those of GM(1,1) model respond more due to the usage of the latest four data. And the residues of the two models are opposite in a statistical sense, according to Wilcoxon signed rank test. So a hybrid model is constructed with these two models, and its MAPE (Mean Absolute Percent Error) is smaller than ARIMA model and GM(1,1) model. And then, China's primary energy consumption is forecasted by using the three models. And the results indicate that the growth rate of China's primary energy consumption from 2014 to 2020 will be rather big, but smaller than the first decade of the new century.