论文研究-基于Matlab支持向量回归机的能源需求预测模型.pdf
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31
10
Vol.31, No.10
2011
10
Systems Engineering — Theory & Practice Oct., 2011
: 1000-6788(2011)10-2001-07
:TK01
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Forecasting model of energy demand based on Matlab support
vector regression
SUN Han, YANG Pu-rong, CHENG Jin-hua
(School of Economics and Management, Chinese University of Geosciences, Wuhan 430074, China)
Abstract This paper analyzes the advantages of support vector regression (SVR) in the prediction of
energy demand, decides the set of input vectors and output vectors and then establishes the model of
prediction of Energy demand by SVR based on Matlab technology modeling and simulation of energy
demand form 1985 to 2008. At last, we apply this method to predict the demand for energy of China in
2010 and 2020. The article drew following conclusions: On the one hand, with the development of economy
of China, the demand of energy will gradually increase. energy increases from 330400 tons in 2010 to 418320
tons 2020, with an annual increase of about 2.39 %. On the other hand, the SVR better than bp neural
network about forecast accuracy, the long existing problem with the small sample
non-linear and pattern
recognition of energy system will be soon solved.
Keywords support vector regression; energy demand; forecast
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