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.

In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance.

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