Based on the EMD and PSO-BP neural network of short-term load forecasting

被引：8
|
作者：
Sha, Feng [1 ]
Zhu, Feng [2 ]
Guo, Shunnan [2 ]
Gao, Jiantong [2 ]
机构：
[1] North China Elect Power Univ, Sch Elect & Elect Engn, Beijing 102206, Peoples R China
[2] NorthEast Dianli Univ, Sch Elect Engn, Jilin 132012, Peoples R China
来源：
ADVANCES IN POWER AND ELECTRICAL ENGINEERING, PTS 1 AND 2 | 2013年 / 614-615卷
关键词：
BP neural network; PSO; short-term load forecasting; empirical mode decomposition;
D O I：
10.4028/www.scientific.net/AMR.614-615.1872
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：
This paper proposes that based on the EMD and PSO-BP neural network of short-term load forecasting. This method will be automatically historical load sequence into several independent intrinsic mode functions (IMF) by using EMD. As the BP neural network training for a long time and easy to fall into local minimum of the shortcomings, we use genetic algorithm to optimize BP neural network algorithm replaces the traditional BP algorithm. Finally, we use the BP neural network optimized separately for each IMF component for training and prediction. We should add the component to the final prediction forecast. This method has higher precision than the EMD-BP model prediction by Simulation results show.
引用
收藏
页码：1872 / +
页数：2
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