## 2 Related Works
For example, the double sampling transformer (DSformer) [49] and the multi-granularity spatiotemporal fusion transformer (MGSformer) [50], which are variants of the transformer model, were proposed for multi-variate time series prediction, and proved to be satisfactory and accurate. Since the core part of those models, the attention mechanism, was embedded in the proposed feature analysis module, it would be redundant and too complex to reuse such a model. Taking the above analysis into account, this paper modifies the GRU model as the external forecasting framework and embeds the heterogeneous graph attention mechanism into GRU, which is efficient and effective. In addition, except for the impact of multiple related features and the model structure, the changing data flow also has an effect on the prediction accuracy. The question of how to dynamically regulate the model in real time and make it more suitable for the current data also appeals to various researchers [51]. [PERSON] et al. [52] utilized data augmentation to process acoustic features related to wind power so as to realize real-time modeling. However, the data augmentation method is not stable enough and is seldomly ineffective, resulting in large deviations in the results. [PERSON] et al. [53] proposed a real-time wind power forecasting model based on a modified LSTM model and a random searching optimization method, but, in fact, it could not dynamically and sufficiently adapt the model to the changing data flow. [PERSON] [54] and his team proposed Powerformer to achieve a dynamic wind power generation forecast, which addressed the complexity of the issue and enhanced feature extraction capability. However, the dynamic property of the model is embodied in the weather prediction and system operation, and the changing relationship among features resulting from the changing data flow has been not valued. Therefore, it is urgent to propose a method that is able to address and model the changeable relationship of the data features. In this paper, a flow-aware dynamic graph learning method is designed to solve this problem which directly employs the input data to adjust and generate the graph structure dynamically. In order to realize dynamic wind power prediction and further enhance the accuracy and stability of the forecasting model, this study raises a new DHGAR model that has the ability to make rational use of the related features and dynamically regulate the model parameters according to data flow. **The detailed innovation and contributions of the proposed model can be summarized as follows:**  
1. In addition to weather features such as wind speed, wind direction, and temperature, the proposed model also incorporates generator-related conditions, including generator speed, rotor speed, and current. This ensures that the prediction model is supplied with comprehensive and sufficient information. 2. Aiming at making full use of the multi-variate data, a heterogeneous graph attention mechanism is proposed. By forming the relationship among features into a directed graph, the model complexity is reduced, while the connections become more interpretable. The graph attention mechanism effectively learns the weighting of different features, uncovering their interrelationships. Additionally, a heterogeneous function is integrated into the graph attention mechanism, enabling the analysis of heterogeneous characteristics from diverse sources. 3. A novel flow-aware dynamic graph learning method is proposed to link the generated graph with real-time data. Since the relationships among features can be influenced by changing data, it is essential to dynamically adjust the forecasting model in response to current data. 4. Taking GRU as the external predictor and replacing its linear layer by the dynamic heterogeneous graph attention mechanism, the proposed wind power forecasting model achieves greater efficiency and effectiveness in practice and can effectively utilize the abundant information embedded in the features.