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Wind speed prediction is critical to the management of wind power generation.Due to the large range of wind speed fluctuations and wake effect, there mayalso be strong correlations between long-distance wind turbines. Thisdifficult-to-extract feature has become a bottleneck for improving accuracy.History and future time information includes the trend of airflow changes,whether this dynamic information can be utilized will also affect theprediction effect. In response to the above problems, this paper proposesWindformer. First, Windformer divides the wind turbine cluster into multiplenon-overlapping windows and calculates correlations inside the windows, thenshifts the windows partially to provide connectivity between windows, andfinally fuses multi-channel features based on detailed and global information.To dynamically model the change process of wind speed, this paper extracts timeseries in both history and future directions simultaneously. Compared withother current-advanced methods, the Mean Square Error (MSE) of Windformer isreduced by 0.5\% to 15\% on two datasets from NERL.

Windformer:Bi-Directional Long-Distance Spatio-Temporal Network For Wind Speed Prediction