Abstract
Considering the complexity and nonlinear characteristics of China’s energy consumption system, neural networks and time series are used to establish individual forecasting models for China’s energy consumption system, and each of the models was tested. The results showed that the models could be used as effective tools to predict China’s future energy consumption. According to standard deviation method, suited weight was distributed to the prediction of each individual model, then a combination forecasting model was established. The combination model not only gets rid of defects of the former models, but it raised the accuracy of the prediction. Then the combination model was applied to predict China’s energy consumption in the next six years. By 2015, China’s energy consumption will be 4.19 billion tons of standard coal.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
The national statistics bureau of the PRC China statistical yearbook. China Statistics Press, Beijing (2008)
The national statistics bureau of the PRC National economy and social development statistics bulletin (2009)
Wang, Z.L., Hu, Y.H.: Applied time series analysis. Science Press, Beijing (2007)
Yu, C.H.: Pass and statistics analysis. Electronic Industry Press, Beijing (2007)
Liu, L.F., Yi, X.J.: Estimation of china energy demand and predict stimulate. Shanghai University of Finance and Economics Journal 10(4), 84–91 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Xy., Lei, Zj. (2012). Prediction of China’s Energy Consumption Based on Combination Model. In: Cao, BY., Xie, XJ. (eds) Fuzzy Engineering and Operations Research. Advances in Intelligent and Soft Computing, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28592-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-28592-9_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28591-2
Online ISBN: 978-3-642-28592-9
eBook Packages: EngineeringEngineering (R0)