Medium and Long-Term Load Forecasting Based on PCA and BP Neural Network Method

被引：7

作者：

Zhang, Shi ^{[1
]}

Wang, Dingwei ^{[1
]}

机构：

[1] Northeastern Univ, Coll Informat Sci & Engn, Inst Syst Engn, Shenyang, Peoples R China

来源：

2009 INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT TECHNOLOGY, VOL 3, PROCEEDINGS | 2009年

关键词：

principal component analysis; back-propagation neural network(BPNN); load forecasting; power system;

D O I：

10.1109/ICEET.2009.559

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：

To settle the problem which the precision and genera-lization performance of forecast model is affected easily by input variable, the method which reconstructs the original input space of back-propagation neural network by principal component analysis that can eliminate the relevance of value is researched. The method can not only reduce duplicated information but also extract the leading factors. Its can also optimize its network structure as well as enhance the network's forecast precision. The effectiveness of the proposed algorithm is verified by the practical data.

引用

页码：389 / 391

页数：3

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