Power curve estimation and feature importance quantification for offshore wind turbines based on XGBoost regression

İngilizce (OpenAlex)

Offshore wind power is a critical component of the global transition to renewable energy. However, the accuracy of power curve prediction, essential for both resource assessment and operational monitoring, is significantly hindered by the unique challenges of the marine environment, such as volatile wind conditions and complex nonlinear turbine dynamics. To overcome these limitations, this study presents a novel framework with a twofold methodological contribution. First, a meticulously optimized eXtreme Gradient Boosting (XGBoost) model is developed, establishing a new state-of-the-art performance benchmark for predicting offshore wind power using only standard environmental sensor data. Second, this high-performing model is leveraged to conduct a novel comparative analysis that reveals the fundamentally different feature dependencies of offshore versus inland turbines. This analysis uncovers the distinct environmental drivers crucial for context-specific modeling, an insight previously unexplored in the literature. Validation against real-world data demonstrates the model’s superiority; the proposed XGBoost approach achieved a Root Mean Square Error (RMSE) of 0.07422 for offshore prediction. This represents a significant performance improvement, reducing the error by 4.7% compared to the next-best model, k-Nearest Neighbor regression (kNN, RMSE 0.0777), and by up to 39% compared to the traditional Binning method (RMSE 0.12117). Consequently, the engineering value of this work lies in its dual achievement: it significantly improves the accuracy of power curve modeling for crucial industry tasks while accomplishing this with low-cost, readily available data. This positions the proposed approach as a practical and economically viable tool for enhancing the operational efficiency and reliability of offshore wind farms.