Prediction on China's energy demand based on RBF neural network model

被引：1

作者：

Feng, Xue ^{[1
]}

Bao, Wuyunbilige ^{[1
]}

Ha, Ben ^{[1
]}

机构：

[1] Xian Univ Sci & Technol, Xian 710054, Shaanxi, Peoples R China

来源：

ENERGY AND POWER TECHNOLOGY, PTS 1 AND 2 | 2013年 / 805-806卷

关键词：

Influencing factors of energy demand; path analysis; RBF neural network model; prediction of energy demand;

D O I：

10.4028/www.scientific.net/AMR.805-806.1421

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, REF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

引用

页码：1421 / 1424

页数：4

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