Volume 25 Issue 3
Jun.  2025
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ZHANG Ying-chao, AN Ran, CHEN Xin, YE Xiao-ling, XIONG Xiong. Wind speed prediction along high-speed railway based on multi-time-interval wind speed fluctuation process division[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 362-379. doi: 10.19818/j.cnki.1671-1637.2025.03.024
Citation: ZHANG Ying-chao, AN Ran, CHEN Xin, YE Xiao-ling, XIONG Xiong. Wind speed prediction along high-speed railway based on multi-time-interval wind speed fluctuation process division[J]. Journal of Traffic and Transportation Engineering, 2025, 25(3): 362-379. doi: 10.19818/j.cnki.1671-1637.2025.03.024
shu

Wind speed prediction along high-speed railway based on multi-time-interval wind speed fluctuation process division

doi: 10.19818/j.cnki.1671-1637.2025.03.024
  • 1.

    School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

  • 2.

    Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Funds:

National Natural Science Foundation of China 42275156

National Natural Science Foundation of China 42205150

Natural Science Foundation of Jiangsu Province BK20210661

More Information
  • Corresponding author: YE Xiao-ling (1964-), female, professor, master, xyz.nim@163.com
  • Received Date: 2024-07-09
  • Accepted Date: 2025-04-02
  • Rev Recd Date: 2025-03-06
  • Publish Date: 2025-06-28
    Abstract
  • To improve the accuracy of wind speed prediction along high-speed railway, realize prediction and warning, provide a sufficient time window for high-wind dispatch, and ensure the operation safety of the high-speed railway in high-wind scenarios, a wind speed prediction method for multi-temporal attention deep echo state network along the high-speed railway based on wind speed fluctuation process division (MT-RFVMD-OD-Fu-Attention-DeepESN) was proposed. By using sampling data of second-level and three-minute wind speed of wind speed monitoring stations along the high-speed railway, as well as the improved radio frequency variational mode decomposition (RF-VMD), the wind speed signals of two resolutions were decomposed and reconstructed into trend components and pulsation components. The frequency analysis was performed by continuous wavelet transform (CWT) to find the variation cycle and divide the two components into time series units of equal length. The clustering algorithm of physical characteristics of wind speed in the units and K-shape fusion was used to divide the two groups of trend components into processes to form a database for wind speed fluctuation processes. Finally, an algorithm for similarity-optimized dynamic time warping (Op-DTW) was designed. The algorithm was used to select wind speed time series fragments with high similarity at two resolutions in the fluctuation process database as training sets, which were input into the designed multi-temporal attention deep echo state prediction network (Fu-Attention-DeepESN). The experiment was verified by using wind speed monitoring data from three wind speed stations along the Beijing-Shanghai high-speed railway and compared with the existing popular methods for wind speed prediction. Analysis results show that the root mean square errors (RMSEs) of wind speed prediction in stations K1005, K1245, and K1066 were 0.234, 0.282, and 0.306, respectively, and the mean absolute percentage errors (MAPEs) were 2.76%, 2.27%, and 2.99%, respectively. The positive wind speed errors (PWSEs) were 0.008, 0.021, and 0.034, respectively. Compared with those of the best of the compared methods, the evaluation indices RMSE, MAPE, and PWSE of the proposed method are reduced by 27.8%, 34.6%, and 27.1% on average, respectively. This proves that this study can effectively handle the complex and non-linear patterns in the second-level wind signals along the high-speed railway and improve the prediction accuracy and adaptability.

     

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