## 1 Introduction  
Since entering the 21 st century, the issue of energy consumption has garnered widespread attention throughout the world [1]. To solve this issue, renewable energy (e.g., wind energy) comes to prominence. Currently, more and more countries are beginning to vigorously establish wind farms. The core of a wind farm is the turbine network, which consists of multiple turbines. Each turbine is treated as a node in the network. By coordinating the operation of multiple turbines, the turbine network maximizes the efficiency in power output and energy utilization. In the turbine network, wind speed prediction, i.e., forecasting future wind speeds based on historical wind speed sequences, has a critical impact. For instance, wind speed prediction promotes the adjustment of the turbine settings for maximizing energy production [2, 3]. Wind speed prediction allows operators to schedule maintenance during low wind speeds, reducing downtime and improving the efficiency of the turbine network [4, 5]. In view of this, accurate wind speed prediction is crucial for the efficient operation of the turbine network, which can enhance energy output efficiency and reduce operational costs. Meanwhile, accurate wind speed prediction is beneficial to optimize turbine maintenance schedules, thus extending the turbine lifespan and enhancing grid stability. In fact, several works have demonstrated that wind speed is highly forecastable [6; 7; 8; 9; 10; 11; 12]. In the early stage, some researchers proposed physical models, such as the Numerical Weather Prediction (NWP) model, to predict wind speed [6]. However, these models have an excessively high computational cost. In view of this, some classic statistical models like support vector regression (SVR) [7] and Auto Regression Integrated Moving Average (ARIMA) [8] are presented. Unfortunately, the real environment is complex and uncertain. Wind speed is affected by multiple factors. In detail, given a turbine network, each turbine takes responsibility for collecting the wind speed at its location. The wind speed at each turbine has an effect on its neighbors, described as spatial dependencies. For instance, in a turbine network, the wind turbine located upwind of others can create a \"wake\" effect, where the wind speed is reduced in its wake due to turbulence and energy loss. This reduction in wind speed can affect the wind speed of downstream turbines due to several factors like the distance between turbines and the configuration of the turbine network. Meanwhile, the past wind speed at a turbine significantly impacts its future, labeled as temporal dependencies. Spatial dependencies and temporal dependencies mutually influence each other and dynamically change over time. We collectively refer to spatial dependencies and temporal dependencies as spatio-temporal correlations. These statistical models are not capable of capturing spatio-temporal correlations. Recently, deep learning technology has exhibited great potential in feature mining. Many researchers employ deep learning models (e.g., Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), and Transformer) to estimate wind speed [9; 10; 11; 12]. For example, [PERSON] et al. proposed an LSTM-based model to predict the wind speed of a monitoring site over 12 h in Sharvi Province [9]. Nonetheless, the LSTM-based model fails to capture spatial dependencies. To tackle this issue, studies in [10] presented a Convolutional Neural Network and the Long Short-Term Memory (CNN-LSTM) for short-term wind speed forecasting, in which a CNN is employed to extract spatial dependencies. Unfortunately, CNNs treat a turbine network as a 2D matrix, resulting in losing significant spatial information, such as relationships between distant turbines [13; 14]. Hence, [PERSON] et al. proposed Graph Convolutional Network-Long Short-Term Memory (GCN-LSTM) for short-term wind power prediction [11].