International Journal of Maritime Technology

en Operational Forecasting for an Offshore Wind Turbine: Benchmarking Data-Driven against Physics-Informed Machine Learning Offshore Hydrodynamic مقاله پژوهشي Research Paper Accurate forecasting of operational parameters is essential for predictive maintenance and digital twinning of offshore wind turbines. Using a unique dataset from the Levenmouth 7MW 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 = 0.2976, R² = 0.9731) 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.   Floating Offshore Wind Turbine (FWOT),Physics-Informed Neural Networks (PINN),Gradient Energy Theory (GET),Digital Twin,Hybrid ML 48 59 http://ijmt.ir/browse.php?a_code=A-10-8407-2&slc_lang=en&sid=1 Kimia Nazarizadeh k.nazarizadeh@outlook.com 10031947532846004503 10031947532846004503 No Babol Noshirvani University of Technology, Babol, Iran Hashem Nowruzi h.nowruzi@nit.ac.ir 10031947532846004504 10031947532846004504 Yes Babol Noshirvani University of Technology, Babol, Iran