In order to better extract and study the characteristics of the wind speed in time-domain and frequency-domain, so as to solve the time-domain randomness and frequency-domain complexity problems of the wind speed signal, a combined short-term prediction model (WD-VMD-DLSTM-AT), which is based on two-stage decomposition (WD + VMD), double long-short-term memory network (DLSTM) and attention mechanism (AT), is proposed; on this basis, a multi-input multiple output (MIMO) codec model based on attention mechanism (MMED-AT) is proposed for multiple short-term wind speed step forecast. Through experimental comparison and analysis, the proposed combined forecasting model has the smallest statistical error and the best prediction accuracy; the MMED-AT models based on the combined model can obviously eliminate the cumulative error of recursive multistep prediction and further improve the stability of multistep prediction.

中文翻译：

---

基于两阶段分解和LSTM的短期风速多步组合预测模型

为了更好地提取和研究风速在时域和频域上的特征，解决风速信号的时域随机性和频域复杂性问题，提出了一种组合短期预测模型提出了基于两阶段分解（WD+VMD）、双长短期记忆网络（DLSTM）和注意力机制（AT）的（WD-VMD-DLSTM-AT）；在此基础上，提出了一种基于注意力机制（MMED-AT）的多输入多输出（MIMO）编解码模型，用于多短期风速步长预测。通过实验对比分析，所提出的组合预测模型具有最小的统计误差和最佳的预测精度；