[1]常雨芳,张力,谢昊,等.采用小波分析和神经网络的短期风速组合预测[J].华侨大学学报(自然科学版),2019,40(4):556-560.[doi:10.11830/ISSN.1000-5013.201806023]
　CHANG Yufang,ZHANG Li,XIE Hao,et al.Short-Term Wind Speed Combined ForecastingUsing Wavelet Analysis and Neural Network[J].Journal of Huaqiao University(Natural Science),2019,40(4):556-560.[doi:10.11830/ISSN.1000-5013.201806023]
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采用小波分析和神经网络的短期风速组合预测()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第40卷
期数:
2019年第4期
页码:
556-560
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Short-Term Wind Speed Combined ForecastingUsing Wavelet Analysis and Neural Network
文章编号:
1000-5013(2019)04-0556-05
作者:
常雨芳1; 2;  张力2;  谢昊2;  刘光裕2
1. 湖北工业大学 太阳能高效利用湖北省协同创新中心, 湖北 武汉 430068;2. 湖北工业大学 电气与电子工程学院, 湖北 武汉 430068
Author(s):
CHANG Yufang1; 2;  ZHANG Li2;  XIE Hao2;  LIU Guangyu2
1. Hubei Collaborative Innovation Centre for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China; 2. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
关键词:
短期预测;  小波分析;  径向基神经网络;  Elman神经网络;  广义回归神经网络
Keywords:
short-term forecasting;  wavelet analysis;  radial basis function neural network;  Elman neural network;  general regression neural network
分类号:
TM614
DOI:
10.11830/ISSN.1000-5013.201806023
文献标志码:
A
摘要:
为了提高风速的波动性与随机性预测精度,提出小波分析和神经网络组合的风速预测模型.该方法利用小波分解将风速分解为一列频率不相同的分量,并利用二插值进行重构;根据各个分量的频率特征,选择合适的模型分别进行预测;高频分量采用组合神经网络预测,低频分量采用合适的单一模型直接进行预测;将各预测值叠加得到最终预测值.算例分析表明:相较于单一预测模型,所提方法的预测精度得到大幅提升,更加贴近实际风速曲线,预测结果更具可靠性.
Abstract:
In order to improve the forecasting accuracy and reduce the impact of randomness and volatility of the wind speed, a new forecasting model based on wavelet analysis and neural network is presented. By means of the wavelet analysis technique, the original wind speed series are decomposed into a series of components of different frequencies components, and reconstructed by twice-interpolation. According to the frequency characteristics of each component, the suitable model is selected to forecast separately. The high frequency component is forecasted by combined neural network, and the low frequency component is forecasted by a suitable single model. The final forecasting value is obtained by superimposing each forecasting values. The simulation results show that compared with the single prediction model, the forecasting accuracy of the proposed method is greatly improved, and it is closer to the actual wind speed curve, and it is more reliable.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2018-06-23
通信作者: 常雨芳(1980-),女,副教授,博士,主要从事智能配电系统和微网能源系统的优化设计与控制的研究.E-mail:changyf@hbut.edu.cn.
基金项目: 国家自然科学基金资助项目(61601176); 湖北省自然科学基金资助项目(2016CFB512)
更新日期/Last Update: 2019-07-20