## 2 Related Works
However, these approaches can increase model complexity to some extent, tend to overfit, and capture the relationships between features inadequately. Therefore, some studies attempt to improve overall model efficiency and performance by employing feature preprocessing or pre-training methods. [PERSON] used fractional order neural networks to preprocess and fill in the missing data, which enhanced the final prediction results. [PERSON] et al. [34] employed multi-variate VMD and elastic net to preprocess the original features and select useful ones, and then performed CNN and LSTM model to obtain prediction results. The experiments demonstrate its effectiveness, but feature selection methods are usually unstable when the importance of features varies with the data flow. [PERSON] et al. [35] proposed an interpretable AI solution with interactive features, which improved the interpretability of the prediction model and could effectively employ the information contained in various features. [PERSON] et al. [36] proposed a convolution attention fusion block to achieve feature fusion, and also used a deep temporal LSTM model for prediction, which reached satisfying results. Like [PERSON]'s models, in [37; 38], the authors have also explored the attention mechanism in wind data analysis and demonstrated its effectiveness. effectiveness. However, the above methods may ignore the basic connections among the features and increase model complexity. To address this issue, graph attention can be a good choice, which is more efficient and interpretable. In common sense, graph learning is always used to deal with spatial correlation or process features of graph form [39]. [PERSON] [40] and his team utilized graph attention to realize large-scale wind power prediction. By taking different grid nodes as graph nodes, and distance as edges, the method gave a good framework for large-scale wind power prediction. [PERSON] and [PERSON] [41] proposed a spatially transferable model to realize wind power prediction and achieved good performance, but it ignored the correlation among environmental features. [PERSON] et al. [42] combined a graph neural network with a gated dilated inception network, which was useful for spatial wind power forecasting, but the feature analysis was ignored in the study and restricted the model performance. To the best of our knowledge, most of the studies using graph learning in wind power prediction concentrate on spatial correlation modeling [43; 44], while very few studies have been conducted on using graph learning to achieve multiple feature analysis. Due to the efficiency and practicality of graph learning, it is meaningful and effective to explore the value of graph attention learning on multi-variate wind power forecasting. In addition, among all wind power-related features, relationships can be complex and heterogeneous. For example, relationships between the features of wind turbines must be different from those among the weather features. Therefore, to fill this gap, a heterogeneous graph attention mechanism is designed in this paper. As for the final predictor, there have been many effective models proposed by researchers around the world. As discussed in the references above, LSTM, TCN, and RNN have been widely accepted. Those methods have advantages in long-term relationship extraction, but some of them are too complicated [45]. Therefore, some scholars adopted Gated Recurrent Unit (GRU) as the predictor, which is more efficient and also has the ability to deal with long time series [46]. [PERSON] [47; 48] and his team designed explainable models for sensitivity analysis and efficient learning, which could discover the changing trend in the time series. But their performance on wind power analysis still remains to be validated. Recently, many models based on an encoder-decoder structure, also known as transformer-based models, have become popular in time series forecasts.