## 2 Related Works  
Wind power forecasting is significant for grid regulation, but the nonlinear and fluctuating nature of wind power presents challenges for modeling [17]. Moreover, it is highly influenced by environmental factors, which further increases the difficulty of prediction. Plenty of scholars have conducted in-depth research on how to improve the accuracy of wind power forecasting. It has been found that the proper utilization of multiple environmental factors and the design of effective model structures are key to enhancing the accuracy and stability of wind power forecasting [18], and these are the two main aspects emphasized in this study. In addition, this research also considers the dynamic impact of data flow changes on model performance. Initially, single-factor forecasting methods were applied to the field of wind power prediction which extract temporal variation information from historical wind power sequences and work out prediction results. After extensive research, many single-factor forecasting models suitable for wind power prediction have been proposed. [PERSON] [19] and his team modified Autoformer with Mixture of Experts (MOE) decomposition to adapt it to long-term univariate wind speed forecasting, which proved to be useful. [PERSON] and [PERSON] [20] proposed a univariate sequence-to-sequence model to achieve wind production prediction. It was effective in short-term and mid-term forecasts, but the accuracy decreased when the forecast steps were increased. [PERSON] and [PERSON] [21] utilized enhanced wavelet transform to preprocess the wind power series, and then adopted the autoregressive integrated moving average with the explanatory variable (ARIMAX) method to obtain prediction results. Although the transformed wind power data were easier to model, the performance of the whole model was still limited due to a lack of external factors and the linear predictor. Several other univariate models [22; 23] have been proposed in this field, but since the environmental features have not been taken into consideration, the performance of these methods is restricted. Therefore, the multi-variate wind power forecast is developed to alleviate this bottleneck. By taking external features into account, like wind speed, wind direction, generator conditions, and so on, the basic input information is more abundant, thereby improving the model performance [24]. [PERSON] et al. [25] employed wind turbine monitoring data and numerical weather prediction (NWP) data and designed a new wind power prediction framework. The NWP data proved to be effective in wind power forecasting. [PERSON] et al. [12] proposed a new neural network model that utilized spatial information and took several wind power series from the different stations as inputs. The performance of this spatial-temporal model was satisfying, but there is still room for enhancement in terms of model structure. In [PERSON]'s research [26], generator conditions like temperature, generator speed, and rotor speed were considered and proved to be helpful. Plenty of other studies [27; 28; 29] on discovering and extracting useful external features have also been conducted, and it is evident that the use of environmental factors allows for promotion in the whole model's performance. However, more related features always lead to more information redundancy [30]. It is crucial to explore ways of rationally and carefully using the information contained in those features. It is urgent to design an appropriate model structure that can make full use of the information provided in the features and get rid of redundant characteristics. Plenty of scholars have put effort into the improvement of multi-variate prediction models and achieved some positive results. Firstly, due to the changeable dimensions of neural networks, many single-factor forecasting models have been directly applied to the multi-factor forecasting domain, such as LSTM [31], recurrent neural networks (RNNs) [32], and temporal convolutional networks (TCNs) [33].