This paper presents a modified S‐transform (ST) based on a compactly supported kernel. A version of Cheriet‐Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S‐transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency‐based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural‐network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short‐term forecasting results.

中文翻译：

---

使用具有紧密支持的内核的S变换进行短期风速预测

本文提出了一种基于紧密支持的内核的改进的S变换（ST）。为此选择了Cheriet-Belochrani（CB）内核版本。结果表明，所提出的改进的S变换（CBST）提供了比传统ST更好的频率分辨率。它用于将风速时间序列分解为基于频率的子序列。此外，将人工神经网络（ANN）应用于每个子系列，提前一个小时进行预测。最后，通过结合所有子序列的预测结果获得原始风速序列的预测。最初，增加子系列的数量会导致预测误差的减小。但是，当子序列的数量足够大时，如果进一步增加该数量，则预测误差不会有显着变化。还观察到，对于基于神经网络的模型，涉及风速时间序列的分解，所提出的模型具有较低的预测误差。对基于小波变换（WT）和经验模态分解（EMD）的方法进行的比较研究证明了该方法的有效性。在本研究中，我们使用了非静水中尺度模型生成的模拟风速数据以及使用风速计和LiDAR仪器在不同高度记录的数据来评估短期预报结果。