## 2 Related Work  
### Wind Speed Prediction  
Wind speed prediction has been explored for decades. In the early stage, some researchers presented physical models (e.g., the NWP model), which intend to model the atmosphere based on physical theory [6]. For instance, [PERSON] et al. combined the forecasted grids of NWP with the monitoring data of wind farms to predict wind speed in the next 96 timesteps [21]. However, the computational complexity of these models is high, which leads to an excessively long model training time. To solve the problem, several studiesemployed some classic statistical models (e.g., ARIMA [8], SVR [7], and K-Nearest Neighbor (KNN) [22]) to perform wind speed prediction. The key idea behind these approaches is using statistical probability methods to predict the time mode from past data. For example, [PERSON] et al. leveraged an optimized SVR to carry out long-term wind speed forecasting [7]. [PERSON] et al. proposed a hybrid model that integrates an artificial neural network and Kalman filter based on ARIMA for daily wind speed prediction in Iraq and Malaysia [8]. [PERSON] et al. predicted a wind speed parameter in n-tupled inputs using KNN classification and analyzed the effects of input parameters, nearest neighbors, and distance metrics on wind speed prediction [22]. Unfortunately, these models cannot mine in-depth spatio-temporal correlations due to discrete learning modules and manual feature selection.  
Recently, deep learning methods have revolutionized many fields, particularly in wind speed prediction [9; 10; 11; 12; 23; 24]. In detail, [PERSON] et al. proposed an encoder-decoder architecture with an LSTM unit, in which the encoder is used to extract temporal dependencies, and then the decoder is leveraged to generate wind speed prediction results over 12 h [9]. [PERSON] et al. used the Temporal Convolutional Network (TCN) as the main wind speed prediction model. Based on experimental results, it can be proved that a TCN can establish satisfactory wind speed prediction results [23]. Unfortunately, LSTM and TCNs neglect the spatial dependencies among turbines. Hence, several studies considered a turbine network as a 2D matrix form and used a CNN to mine spatial dependencies [10]. Although CNNs have certain advantages in modeling spatial dependencies, the convolution function cannot identify some vital spatial relationships, such as dependencies between distant turbines. To address the issue, the GCN has received a lot of attention since it regards a turbine network as a graph, which completely preserves the natural spatial associations between turbines. Some works strived to use GCN-based methods to mine spatio-temporal correlations. The studies in [11] presented a hybrid framework called GCN-LSTM for short-term wind power prediction, in which a GCN and LSTM are adopted to model spatial dependencies and temporal dependencies, respectively. The studies in [24] proposed a network called the Graph Temporal Convolutional Network, which integrates a GCN and TCN to extract features in both spatial and temporal dimensions of wind speed data in a wind farm for wind speed predictions. With the creation of a Transformer, the self-attention's potential in modeling spatio-temporal characteristics is discovered. [PERSON] et al. presented GAT-Informer, which integrates a GAT and Informer to predict wind speed from two wind farms in Xinjiang Province, China [12]. In contrast to GCN-based models, the Transformer-based models have the ability to capture spatio-temporal correlations more effectively, further improving prediction precision. Unfortunately, the Transformer includes a vast amount of parameters that need to be trained. This results in high computational costs.