Abstract
Variable weight combination forecasting combines the individual forecasting models after giving them proper weights at each time point. Weight is the kind of function which changes with the forecast time. A relatively rational description of objective fact of the system can be proposed with the forecasting method which is a higher precision and a better stability. Two individual forecasting models, the Grey system forecasting and the multiple regression forecasting, are constituted based on the historical data and the influencing factors of the coal demand in China from 1981 to 2008 in this paper. And based on the theory of combination forecasting, the variable weight combination forecasting model is formulated for the coal demand to forecast the coal demand of China in the coming 12 years.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lu Erpo. 2005. A study of the forecasting modal of Chinese energy demand. Statistics and Decision, 20: 31–33. (in Chinese).
Guo Guntao. 2004. Analysis and prediction of medium and long term coal supply and demand in China. China Coal, 10: 22–25. (in Chinese)
Chen Huayou. 2001. Research on combination forecasting model with non negative and time-variant weights based on effective measure of forecasting methods. Operations Research and Management Science, 1: 50–54.
Kouris, George. 1998. Energy consumption and economic activity in industrialized countries: a note. Energy Economics, 5: 207–212.
Lu Qi, Gu Peiliang, Qiu Shiming. 2003. The construction and application of combination forecasting model in Chinese energy consumption system. Systems Engineering-Theory & Practice, 3: 25–31. (in Chinese).
Jin Quanzhong, Zhang Jian. 2004. GM (1, 1) Application of the modal in the forecasting of the coal demand. China Coal, 1: 17–19. (in Chinese)
Shiying Dong. 2000. Energy demand projections based on an uncertain dynamic system modeling approach. Energy Sources, 7: 443–451.
Wang Jing, Liu Liangdong, Wang Zuoyi. 1997. Current situation and development of combination forecasting model. Forecasting, 6: 7–38. (in Chinese).
Weigen, Andress S. 1996. Time series and prediction using gated experts with application to energy demand forecasts. Applied Artificial Intelligence, 11–12: 583–624.
Tang Xiaowo, Zeng Yong, Cao Changxiu. 1993. Study of time-variant weight combination forecasts. Forecasting, 3: 48–50. (in Chinese).
Xie Kaigui, Zhou Jiaqi. 2003. Combination forecasting models using regression analysis method. Journal of Chongqing University (Natural Science Edition), 1: 64–67. (in Chinese).
Zhao Liming, Qian Weirong, Shen Yanfeng. 2001. Combination of forecasts for demand of dwelling market. Journal of Systems Engineering, 3: 66–69. (in Chinese).
Acknowledgements
The research was supported by National Natural Science Foundation in China (No. 70873079) and (No. 70941022), Shanxi Natural Science Foundation (No. 2009011021-1) and Shanxi international science and technology cooperation foundation (2008081014).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, G., Guo, S., Shentu, J., Wang, Y. (2011). An Investigation of the Coal Demand in China Based on Variable Weight Combination Forecasting Model. In: Wu, D. (eds) Modeling Risk Management in Sustainable Construction. Computational Risk Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15243-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-15243-6_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15242-9
Online ISBN: 978-3-642-15243-6
eBook Packages: Business and EconomicsBusiness and Management (R0)