[1]王 宁,罗汝斌,廖 俊,等. 基于小波包分解的BP 神经网络的短期风速预测[J].控制与信息技术(原大功率变流技术),2019,(04):1.[doi:10.13889/j.issn.2096-5427.2019.04.300]
 WANG Ning,LUO Rubin,LIAO Jun,et al. Short-term Wind Speed Prediction Based on Wavelet Packet Decomposition and BP Neural Network[J].High Power Converter Technology,2019,(04):1.[doi:10.13889/j.issn.2096-5427.2019.04.300]
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 基于小波包分解的BP 神经网络的短期风速预测()
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《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年04期
页码:
1
栏目:
出版日期:
2019-08-05

文章信息/Info

Title:
 Short-term Wind Speed Prediction Based on Wavelet Packet Decomposition and BP Neural Network
作者:
 王 宁1罗汝斌2廖 俊1李 珺1蒋 祎1杨泽川1袁俊杰1
 (1. 中南大学 航空航天学院,湖南 长沙 410083;2. 北京宇航系统工程研究所,北京 100076)
Author(s):
 WANG Ning1 LUO Rubin2 LIAO Jun1 LI Jun1 JIANG Yi1 YANG Zechuan1 YUAN Junjie1
 ( 1. School of Aeronautics and Astronautics, Central South University ,Changsha,Hunan 410083, China;
2. Beijing Institute of Astronautical System Engineering, Beijing 100076, China)
关键词:
 态势感知小波包分解BP 神经网络短期风速预测浮空器飞行控制
Keywords:
 situation awareness wavelet packet decomposition BP neural network short-term wind speed prediction aerostat flight control
分类号:
TM614
DOI:
10.13889/j.issn.2096-5427.2019.04.300
文献标志码:
A
摘要:
 针对风速信号不稳定而引起的风速预测精度不高问题,文章提出了一种短期风速预测方法,其通过小波包分解将不稳定的风速信号转化为相对稳定的风速信号,再对其进行BP 神经网络预测,从而提高短期风速预测精度。仿真计算结果表明,基于小波包分解的BP 神经网络的短期风速预测模型的平均绝对百分比误差(MAPE)、均方根误差(RMSE)、平均绝对误差(MAE) 均低于其他短期风速预测方法的各项误差,在短期风速预测中具有一定的优越性。
Abstract:
 In this paper, a short-term wind speed prediction method based on BP neural network and wavelet packet decomposition
is proposed to solve the problem of insufficient accuracy of wind speed prediction introduced by unstable wind speed signals. The unstable wind speed signal is transformed into a relatively stable wind speed signal by wavelet packet decomposition, and the combination with BP neural network successfully improves the accuracy of short-term wind speed prediction. The simulation results show that the average absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the short-term wind speed prediction model based on wavelet packet decomposition are lower than those of other short-term wind speed prediction methods. So, it has certain advantages in short-term wind speed prediction.

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备注/Memo

备注/Memo:
 收稿日期:2019-05-15
作者简介:王宁(1995—),男,硕士,主要从事新概念飞行器设计工作。
更新日期/Last Update: 2019-08-02