短期风速具有间歇性、波动性、非线性和非平稳性等特点，具有高度的复杂性，预测难度较大。风速信号可以看成是由复杂度较低、规律较强的简单信号耦合而成，所以可利用分解方法使之分为多尺度的波动分量，降低分量复杂度，增强其规律性，可以提高其预测精度。因此，为了提高神经网络的学习效率，采用Kmeans算法对原始风速数据进行相似日聚类；其次，使用VMD分解风速序列，提取多尺度规律；最后，由于LSTM神经网络捕捉长时间依赖的序列的波动规律的能力较强，使用LSTM神经网络对分解后的风速分量进行预测，将各分量预测值叠加得到最终预测结果。通过大量试验和不同方法之间的比较表明，基于KmeansVMDLSTM的组合预测模型可以有效提高风速短期预测的准确率。

Short-term wind speed is intermittent, fluctuating, nonlinear and non-stationary, and has a high degree of complexity, which is difficult to predict. The wind speed signal can be seen as coupled from simple signals with low complexity and strong regularity, so the decomposition method can be used to make it divided into multi-scale fluctuating energy, reduce the component complexity and enhance its regularity, which can improve its prediction accuracy. Therefore, to improve the learning efficiency of the neural network, the Kmeans algorithm is used to cluster the original wind speed data on similar days. Secondly, the wind speed sequence is decomposed using VMD to extract the multiscale regularity. Finally, because the LSTM neural network is more capable of capturing the fluctuation regularity of the long-dependent sequence, the decomposed wind speed components are predicted by using the LSTM neural network, and the final prediction results are obtained by superimposing the predicted values of each component. The combination prediction model based on Kmeans-VMD-LSTM can effectively improve the accuracy of short-term wind speed prediction as shown by a large number of experiments and comparisons between different methods.