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 may also be strong correlations between long-distance wind turbines. This difficult-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 the prediction effect. In response to the above problems, this paper proposes Windformer. First, Windformer divides the wind turbine cluster into multiple non-overlapping windows and calculates correlations inside the windows, then shifts the windows partially to provide connectivity between windows, and finally fuses multi-channel features based on detailed and global information. To dynamically model the change process of wind speed, this paper extracts time series in both history and future directions simultaneously. Compared with other current-advanced methods, the Mean Square Error (MSE) of Windformer is reduced by 0.5\% to 15\% on two datasets from NERL.

翻译：风速预测对风能发电管理至关重要。由于风速波动范围大且存在尾流效应，长距离风力发电机之间也可能存在强相关性。这一难以提取的特征成为提升精度的瓶颈。历史与未来时间信息包含气流变化趋势，能否利用这些动态信息也将影响预测效果。针对上述问题，本文提出Windformer。首先，Windformer将风力发电机集群划分为多个非重叠窗口，计算窗口内部相关性，随后部分移动窗口以实现窗口间的连通性，最后基于细节与全局信息融合多通道特征。为动态建模风速变化过程，本文同时从历史与未来方向提取时间序列。与当前先进方法相比，Windformer在NERL两个数据集上的均方误差（MSE）降低了0.5%至15%。