铁道科学与工程学报2025，Vol.22Issue(2)：543-556,14.DOI:10.19713/j.cnki.43-1423/u.T20240720
基于AMSD-WTSSA-DELM模型的铁路沿线短期风速预测方法
Short-term wind speed prediction method along the railroad based on AMSD-WTSSA-DELM model
摘要
Abstract
To solve the problems of low prediction accuracy and poor generalization in wind speed forecasting along Northwestern China's railways caused by strong non-stationarity and stochastic fluctuations,this study proposed a hybrid AMSD-WTSSA-DELM prediction framework.First,the original wind speed series with high non-stationarity,the long-term correlation performance of the components,the underlying patterns,trends and periodicity contained in the components were used to decompose each step,and the decomposition conditions and adaptive update thresholds were established.In order to avoid excessive decomposition,the adaptive refactoring method was added to decompose until there are no high-complexity components,so as to achieve adaptive multi-step decomposition with strong adaptability.Furthermore,the WTSSA algorithm was introduced by integrating chaotic mapping,adaptive weighting and the adaptive t-distribution perturbation strategies are integrated into SSA,which improved the global search and local exploration capabilities of the original SSA,accelerated the convergence speed,and verified the excellence of the WTSSA algorithm through test functions.Then,for each component of AMSD output,a Deep Extreme Learning Machine(DELM)model with WTSSA optimized weights and biases was established.Finally,the forecast data for all components was summarized to synthesize the final forecast output.The experimental results show that the proposed model has a significant improvement effect on the prediction performance of wind speed data along two groups of the actual railway,and the first set of experimental data as an example,the mean absolute error(Emae)and root mean square error(Ermse)of DELM reduced by 90.32%and 82.25%,respectively,and the coefficient of determination(R2)increased by 43.00%.In summary,the prediction model proposed in this paper effectively overcomes the time-lag problem caused by the nonlinear characteristics of wind speed,which has high generalization performance and can predict short-term wind speed changes to help the railway system make more effective safety decisions and provide strong technical support for the safe operation of trains.关键词
短期风速预测/自适应多步分解/深度极限学习机/改进麻雀搜索算法/铁路沿线风速Key words
short-term wind speed prediction/adaptive multi-step decomposition/deep extreme learning machine/improved sparrow search algorithm/wind speed along railroads分类
交通工程引用本文复制引用
尼比江·艾力,张林鍹,李奕超,景雨啸,高金山,王渊,谢明浩,罗晓龙..基于AMSD-WTSSA-DELM模型的铁路沿线短期风速预测方法[J].铁道科学与工程学报,2025,22(2):543-556,14.基金项目
中国国家铁路集团有限公司青年专项课题(Q2023T002) （Q2023T002）
新疆维吾尔自治区自然科学基金资助项目(2022D01C431) （2022D01C431）
