## 1 Introduction  
Due to a shortage of fossil fuels and concerns about environmental pollution, governments around the world have begun to vigorously promote the development of clean energy [1]. Wind and solar energy are currently among the most important clean energy sources [2]. However, wind power generation is characterized by unstable amplitude and frequency and the presence of harmonics, and its output is significantly affected by environmental conditions [3]. These factors make it difficult for wind power to be directly integrated into the grid and it imposes additional burdens on grid operations, includingpeak regulation, frequency adjustment, and voltage control [4]. Therefore, in order to ensure grid security and improve power supply quality, it is of great significance to effectively regulate wind power output currents and mitigate their negative impact on the grid.  
Currently, power distribution networks mainly regulate wind power frequency and output through methods such as generator adjustments, substation control, and energy storage systems [5]. Wind power forecasting plays a crucial role in this process by providing real-time data for the formulation of regulation strategies [6]. With the predicted wind power data, the power system can develop load balancing and frequency regulation plans in advance, and wind turbines can also adjust in real time [7]. Developing a stable and accurate wind power forecasting method is of utmost importance.  
Given that accurate wind power forecasting methods significantly impact the power regulation process, how we can achieve further improvements in this area has become a deeply researched question. Unlike general time series forecasting problems, wind power series exhibit nonlinear characteristics and are strongly influenced by environmental factors [8]. Thus, prediction accuracy improvement cannot rely solely on historical data and model performance. Instead, it requires a comprehensive approach that also considers environmental factors and the characteristics of wind turbines. Additionally, environmental variables such as wind speed and wind direction dynamically influence wind power over time [9]. Capturing this dynamic feature correlation in the model is essential for enhancing forecasting stability.  
Many scholars have conducted extensive research, and various models, such as Long Short-Term Memory (LSTM) [10], hybrid models [9], ensemble models [11], and graph models [12], have been applied in this field. These studies mainly focus on two aspects: data preprocessing and model improvement. Data preprocessing enhances prediction accuracy by transforming data into a format that is easier to learn, like Variational Mode Decomposition (VMD) [13], or by extracting and selecting relevant features, like convolutional neural networks (CNNs) [14]. It is also crucial to improve the model structure and make it more appropriate for wind power prediction. For instance, ensemble models enhance robustness by combining multiple predictors [15] and graph models incorporate information about the spatial relationships between wind turbine locations to improve prediction accuracy [16]. Current research has made advancements in wind power prediction. However, to our knowledge, few studies have examined the dynamic correlation among different features and the heterogeneity in how different environmental factors influence wind power has not been sufficiently addressed. Therefore, to fill this gap, it is necessary to develop a wind power prediction model that makes full use of environmental features and achieves dynamic learning of data correlations, which is the focus of this paper.