Operational Forecasting for an Offshore Wind Turbine: Benchmarking Data-Driven against Physics-Informed Machine Learning

Accurate forecasting of operational parameters is essential for predictive maintenance and digital twinning of offshore wind turbines. Using a unique dataset from the Levenmouth ۷MW demonstration turbine, we compare a purely data-driven stacked ensemble model (StackedRidge) with a novel physics-informed neural network (GET-PINN) that incorporates the Energy Gradient (K) parameter from Energy Gradient Theory (GET). The StackedRidge model achieves superior predictive accuracy (RMSE = ۰.۲۹۷۶, R² = ۰.۹۷۳۱) for barometric pressure signals. In contrast, the GET-PINN provides valuable physics-aware diagnostics by jointly estimating the flow instability parameter K, supporting the detection of phenomena such as vortex-induced vibrations (VIV), albeit with higher forecasting error. These results highlight the complementary strengths of the two approaches: the stacked ensemble for high-fidelity point forecasting and the GET-PINN for interpretable, physics-guided maintenance decision support in operational wind farm digital twins.

کلیدواژه ها:

Floating Offshore Wind Turbine (FWOT) ، Physics-Informed Neural Networks (PINN) ، Gradient Energy Theory (GET) ، Digital Twin ، Hybrid ML