Short-term Wind Power Forecasting by Genetic Algorithm Of Wavelet Neural Network

被引：0
|
作者：
Wang, Yicong [1 ]
机构：
[1] North China Elect Power Univ, Elect Power Engn Inst, Baoding, Peoples R China
来源：
2014 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, ELECTRONICS AND ELECTRICAL ENGINEERING (ISEEE), VOLS 1-3 | 2014年
关键词：
wind power forcast; wavelet neural network; genetic Algorithm;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Wavelet neural network (WNN) is widely used in wind power prediction because of its good self-learning capacity and excellent performance to approach any nonlinear function. However it also has limitation in precision and operating speed. This paper proposes a new genetic algorithm of wavelet neural network (GAWNN) for short-term wind power forecasting in electrical power systems. GAWNN makes a good combination of genetic algorithm and wavelet neural network and makes great process in convergent precision and speed. The experiment results show that GAWNN is more feasible and effective.
引用
收藏
页码：1751 / +
页数：3
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