With the rapid development of new energy technologies and aiming at the proposal of the “DOUBLE CARBON” goal, the proportion of wind energy and other new sustainable energy power solutions in the power industry continues to increase and occupy a more critical position. However, the instability of wind power output brings serious challenges to safe and stable power grid operations. Therefore, accurate ultra-short-term wind power prediction is of great significance in stabilizing power system operations. This paper presents an ACNN-BiGRU wind power ultra-short-term prediction model based on the Attention mechanism, the fusion of convolutional neural network (CNN), and bidirectional gated recurrent unit (BiGRU). The model takes a single wind turbine as the prediction unit and uses the real-time meteorological data in the wind farm, the historical power data of the wind turbine, and the real-time operation data for parallel training. Then, it extracts the key features of the input data through CNN and uses the BiGRU network to conduct bidirectional modeling learning on the dynamic changes of the features proposed by CNN. In addition, the Attention mechanism is introduced to give different weights to BiGRU implicit states through mapping, weighting, and learning parameter matrix to complete the ultra-short-term wind power prediction. Finally, the actual observation data of a wind farm in Northwest China is used to verify the feasibility and effectiveness of the proposed model. The model provides new ideas and methods for ultra-short-term high-precision prediction for wind power.

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To solve the problem that the link prediction method based on local information ignores the influence of neighbor structure information on the similarity measurement of nodes, a link prediction method based on relative entropy and local structure of nodes is proposed. Firstly, the second-order local network is introduced to describe the local structure of nodes; then, the structural similarity between nodes is described by redefining the relative entropy; finally, the structural similarity of nodes is measured based on relative entropy, and the structural similarity index of the node structure is proposed considering the structure information of the neighbor. Experimental results on 7 real network data sets show that the proposed method can achieve better results and can be applied to networks with a small average aggregation coefficient compared with other similarity indexes based on local and global information, and also have better performance on large-scale networks.

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