The purpose of this paper is to find a method that has high precision to forecast the energy consumption of China’s manufacturing industry. The authors hope the predicted data can provide references to the formulation of government’s energy strategy and the sustained growth of economy in China.
First, the authors respectively make use of regression prediction model and grey system theory GM(1,1) model to construct single model based the data of 2001-2010, analyze the advantages and disadvantages of single prediction models. The authors use the data of 2011 and 2012 to test the model. Second, the authors propose combination forecasting model of manufacturing’s energy consumption in China by using standard variance to allocate the weight. Finally, this model is applied to forecast China’s manufacturing energy consumption during 2013-2016.
The result shows that the combination model is a better one with higher accuracy; the authors can take the model as an effective tool to predict manufacturing’s energy consumption in China. And the energy consumption of China’s manufacturing industry continued to show a steady incremental trend.
This method takes full advantages of the effective information reflected by the single model and improves the prediction accuracy.
This work was supported by National Natural Science Foundation of China (71171116) Humanities and Social Sciences Foundation of Ministry of Education of China (09YJC630129).
Yao, T. and Cheng, W. (2015), "The analysis of the energy consumption of Chinese manufacturing based on the combination forecasting model", Grey Systems: Theory and Application, Vol. 5 No. 1, pp. 41-53. https://doi.org/10.1108/GS-11-2014-0044Download as .RIS
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