Ultra-short-term prediction and analysis of wind speed and direction of freestyle skiing aerial skill track

doi:10.7523/j.ucas.2020.0043

Abstract

Abstract: Freestyle skiing aerial skills are the dominant snow sports in China, and the wind has a particularly significant impact on this sport. This article aims to realize ultra-short-term prediction of wind speed and direction on the track, provide practical and effective forecast information for this sport, and provide auxiliary support for athlete stability control and technical training. In view of the non-stationary and violent fluctuations of the track wind, the discrete wavelet transform is used to extract the characteristic components of the wind speed and direction sequence, the NAR neural network model is established for the low-frequency approximate component, and the ARIMA model is established for the high-frequency detail component, and then the results of each component are combined the final prediction result. The error analysis shows that the combined model can effectively improve the prediction lag of the single model, improve the prediction accuracy and have the ability to predict sudden changes in wind speed and direction. The prediction results is further analyzed, and converted into indicators that characterize track wind stability to provide more intuitive forecast information. Finally, the analysis of model calculation time shows that this method can meet the needs of practical applications.

Key words: ski track, wind speed and direction prediction, wavelet transform, NAR dynamic neural network, ARIMA model

CLC Number:

G863.1

DENG Ziwei, SHAO Yun, WANG Guojun, HUANG Fuxiang, YANG Jiaqi. Ultra-short-term prediction and analysis of wind speed and direction of freestyle skiing aerial skill track[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(3): 332-342.

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