Spatiotemporal physics-guided graph neural network for wind farm power prediction

Recent efforts to enhance the interpretability of Graph Neural Networks (GNN) have focused on boosting their trustworthiness and traceability while maintaining predictive accuracy. However, mainstream approaches largely rely on data-driven strategies, with limited integration of physical prior knowledge or critical examination of GNN interpretability within physics-constrained frameworks. This paper introduces a novel Physics-guided GNN that incorporates spatiotemporal wind farm dynamics using a cutting-edge three-dimensional wake analytical model to guide the GNN architecture. This integration ensures compliance with physical laws during training, reducing the uncertainty and complexity associated with purely data-driven learning and addressing scalability challenges. By employing spatiotemporal directed local subgraphs and physics-induced attention weight learning, the model effectively considers the spatiotemporal wake coupling processes in wind farms, enabling high-accuracy power prediction. The proposed model outperforms other neural network structures in predicting both overall wind farm power production and individual turbine output. This research offers an efficient solution for power prediction in complex wind farms and demonstrates the potential of embedding domain-specific physical knowledge into GNN across broader multi-physics scenarios. It provides valuable insights into integrating physical theories with GNNs, enhancing model precision and transparency. • A novel 3D STV-PGNN enhances spatiotemporal prediction in dynamic systems via optimal features, boosting interpretability. • A spatial time-varying directed subgraph strategy addresses wind farm challenges from complex topologies and varying inflow. • Physics-driven attention edge weights and parameter learning enhance prediction accuracy via physical consistency. • Results validate the physics-GNN integration, demonstrating gains in accuracy, robustness, interpretability and scalability.