中国铁道科学2023，Vol.44Issue(6)：80-86,7.DOI:10.3969/j.issn.1001-4632.2023.06.08
基于CEEMDAN-WOA-SVR的高铁沿线超短期风速预测方法
Prediction Method of Ultra-Short-Term Wind Speed along High Speed Railway Based on CEEMDAN-WOA-SVR
摘要
Abstract
To improve wind speed prediction accuracy along railway lines and enhance the ability for monitoring and warning against strong winds,a hybrid model called CEEMDAN-WOA-SVR is proposed.This model is based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and uses the Whale Optimization Algorithm(WOA)to optimize Support Vector Regression(SVR).Firstly,considering the non-stationary characteristics and nonlinear trends of wind speed,CEEMDAN is applied to decompose the wind speed signal and extract modal components at different frequencies.Secondly,WOA is used to optimize the penalty factors and kernel parameters of the SVR model,and a wind speed prediction model is constructed.Finally,taking wind speed measurement points along a high-speed railway in China as an example,the prediction was carried out.The results show that the accuracy of the 3-minute wind speed prediction is improved by 25％compared to the four benchmark algorithm,thus verifying the accuracy of the method,and the prediction accuracy for the 5-minute wind speed is improved by 20％,indicating that the method has better generalization.The proposed method is an effective exploration of wind speed prediction along the high-speed railway.Therefore,the proposed model can provide reference for wind speed monitoring and warning along high-speed railways.关键词
高铁/风速预测/自适应噪声完全集合经验模态分解/鲸鱼优化算法/支持向量回归Key words
High-speed railway/Wind speed prediction/Complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)/Whale optimization algorithm/Support vector regression分类
交通工程引用本文复制引用
王瑞,马祯,李磊..基于CEEMDAN-WOA-SVR的高铁沿线超短期风速预测方法[J].中国铁道科学,2023,44(6):80-86,7.基金项目
中国铁道科学研究院集团有限公司院基金课题(2021YJ140) （2021YJ140）
