The Study of Wind Power Predict Model Based on Wavelet Transform and Elman Neural Network

被引：0
|
作者：
Zhao, Rui [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
来源：
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年
关键词：
Wind Power; Wavelet Transform; Elman Neural Network; MATLAB simulation;
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：
In order to reduce adverse effects of wind power, this thesis studies the wind prediction model, which is the basis for predicting the end of load model. We use the wavelet transform analysis method to decompose the data into five layers, reduce the input, determine the principal components of the wind power process and simplify the structure of Elman neural network for the wind farm which is not stability and has the characteristics of many uncertain factors. According to a wind farm factory which provide 720 groups of sampling data, this paper establish the wind power model. The sampling interval is one hour and we sample a total of one month. Firstly, we use the DB8 wavelet transform to decompose the sampled data and then use the Elman neural network to predict the power of the wind farm. Finally, it is effectively to verify the method after using MATLAB simulation and also the method can significantly improve the prediction accuracy and help to improve the utilization rate of wind power through the comparison.
引用
收藏
页码：6026 / 6030
页数：5
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