针对现有数据驱动预测模型难以充分利用风电场气动耦合等物理知识导致风电功率预测精度受限的问题，提出了一种融合物理信息的风向感知图注意力网络（PI-WaGAT）。模型采用高斯尾流模型对风机间的气动影响进行量化分析，以构建随风况变化的动态拓扑结构。在此基础上，设计风向感知的超网络机制动态生成图注意力权重，并结合混合密度网络与物理条件分布可行域正则化损失函数，自适应地在模型训练中嵌入物理知识。实验表明，该模型的点预测和概率预测精度均优于基准模型，在复杂风况下具有较好的物理一致性。

To address the limitation in wind power forecasting accuracy caused by the difficulty of existing data-driven models in fully utilizing physical knowledge such as aerodynamic coupling in wind farms, a Physics-Informed Wind-aware Graph Attention Network (PI-WaGAT) is proposed. The model employs the Gaussian wake model to quantitatively analyze aerodynamic influences between turbines, constructing a dynamic topology that varies with wind conditions. On this basis, a wind-aware hypernetwork mechanism is designed to dynamically generate graph attention weights. By combining a Mixture Density Network with a physical feasible region regularization loss function based on conditional distributions, physical knowledge is adaptively embedded into the model training process. Experiments show that the proposed model outperforms benchmark models in both point and probabilistic forecasting accuracy, demonstrating good physical consistency under complex wind conditions.