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基于NRBO-VMD-WOA-ELM混合算法的短期风速预测

赵国辉 王珞歆 王峰 李加武

湖南大学学报(自然科学版)2026,Vol.53Issue(7):41-52,12.
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湖南大学学报(自然科学版)2026,Vol.53Issue(7):41-52,12.DOI:10.16339/j.cnki.hdxbzkb.2026059

基于NRBO-VMD-WOA-ELM混合算法的短期风速预测

Short-term wind speed forecasting based on a hybrid NRBO-VMD-WOA-ELM algorithm

赵国辉 1王珞歆 1王峰 1李加武1

作者信息

  • 1. 长安大学 公路学院,陕西 西安 710064
  • 折叠

摘要

Abstract

To address the limitations of existing short-term wind speed prediction methods in terms of accuracy,this study proposes a hybrid prediction model that integrates the variational mode decomposition(VMD),the Newton-Raphson-based optimizer(NRBO),whale optimization algorithm(WOA),and the extreme learning machine(ELM).The penalty factor α and mode number K of VMD are dynamically optimized by NRBO,yielding intrinsic mode functions(IMFs)with well-separated center frequencies and narrow bandwidths,effectively suppressing mode mixing and improving the learning efficiency and prediction accuracy of each sub-sequence.The decomposition produces four high-frequency components(IMF1~IMF4,accounting for 4.95%of total energy)and one low-frequency trend component(IMF5,accounting for 95.05%of total energy),where IMF5ʼs prediction accuracy primarily determines the upper bound of the overall model error through error propagation.The weights and thresholds of ELM for each component are subsequently optimized by WOA to enhance individual component prediction and final signal reconstruction.Validated using field-measured wind speed data at a bridge site,the results demonstrate that,relative to the baseline model WOA-ELM,the introduction of VMD decomposition reduces EMAE by 60.09%,and the subsequent incorporation of NRBO further reduces EMAE by 41.67%.Compared with four conventional single models,EMAE reductions of 80.11%,80.53%,63.79%,and 80.37%are achieved,respectively.Furthermore,WOA optimization of ELM weights and thresholds improves model stability and generalization capability,confirming the effectiveness of the proposed parameter dynamic optimization strategy with demonstrated potential for engineering applications.

关键词

风速预测/牛顿-拉夫逊法/变分模态分解/鲸鱼优化算法/极限学习机

Key words

wind speed prediction/Newton-Raphson method/variational mode decomposition/whale optimiza-tion algorithm/extreme learning machine

分类

交通工程

引用本文复制引用

赵国辉,王珞歆,王峰,李加武..基于NRBO-VMD-WOA-ELM混合算法的短期风速预测[J].湖南大学学报(自然科学版),2026,53(7):41-52,12.

基金项目

国家自然科学基金资助项目(51978077),National Natural Science Foundation of China(51978077) (51978077)

湖南大学学报(自然科学版)

1674-2974

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