Wind Power Output Prediction with BP Neural Network Combining Genetic Algorithms

被引：2

作者：

Pan Xuetao ^{[1
]}

Qu Keqing ^{[1
]}

机构：

[1] Shanghai Univ Elect Power, Shanghai 200090, Peoples R China

来源：

ENERGY DEVELOPMENT, PTS 1-4 | 2014年 / 860-863卷

关键词：

Wind Power; Forecasting; BP Neural Network; Genetic Algorithm;

D O I：

10.4028/www.scientific.net/AMR.860-863.2526

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：

While having been successfully applied in forecasting with many researches, back Propagation (BP) neural network are with problems such as permutation and premature convergence due to dependence on initial connection weights or other parameters. This paper investigates Genetic Algorithms (GA) evolved BP network and its application to wind power forecasting. Sample analysis with daily wind output data demonstrates that GA-based neural network is with better performance.

引用

页码：2526 / 2529

页数：4

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