The prediction model for electrical power system using an improved hybrid optimization model

被引:28
作者
Li, Guo-Dong [1 ]
Masuda, Shiro [1 ]
Nagai, Masatake [2 ]
机构
[1] Tokyo Metropolitan Univ, Dept Syst Design, Tokyo 1910065, Japan
[2] Kanagawa Univ, Dept Engn, Yokohama, Kanagawa 2218686, Japan
关键词
Thermal electric power generation prediction; Grey model; Regression model; Markov chain model; Taylor approximation method;
D O I
10.1016/j.ijepes.2012.08.047
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, an improved hybrid optimization model based on grey GM (1,1) model is proposed to develop the prediction model in power systems. To realize more accurate prediction, the regression model is firstly integrated into GM (1,1) through compensation for the residual error series. The improved model is defined as RGM (1,1). Furthermore, Markov chain model is applied to RGM (1,1) to enhance the prediction performance. We call the proposed model as MC-RGM (1,1). Finally, Taylor approximation method is presented MC-RGM (1,1) for achieving the high prediction accuracy. The improved model is defined as T-MC-RGM (1,1). A real case of thermal electric power generation in Japan is used to validate the effectiveness of proposed model. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:981 / 987
页数:7
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