Physics-constrained graph attention learning for wind turbine power forecasting from SCADA measurements

Abstract

Accurate interpretation of wind turbine operational measurements is essential for reliable wind power forecasting and efficient wind farm operation. Modern wind turbines are equipped with supervisory control and data acquisition (SCADA) systems that continuously record key operational parameters, providing rich measurement data for data-driven modeling. However, although deep learning methods have demonstrated strong capability in capturing nonlinear relationships, most existing approaches lack physical interpretability, show limited robustness under extreme conditions, and often neglect fundamental aerodynamic constraints. To address these limitations, this paper proposes a physics-constrained graph attention network (PCGAT) for multi-horizon mid-to-long term (from 6 h to 3 d) wind turbine power forecasting using SCADA measurement data. The proposed model constructs a temporal graph in which each node represents a measurement time step and connects to its preceding neighbors to capture dynamic dependencies, while a multi-head graph attention mechanism extracts informative representations from the graph-structured time series. A hybrid loss function incorporating aerodynamic constraints is introduced to ensure physical consistency during model training. Experiments on real-world SCADA datasets demonstrate that the proposed approach achieves higher forecasting accuracy and stronger robustness than several state-of-the-art models across multiple prediction horizons.