RegStack machine learning model for accurate prediction of tidal stream turbine performance and biofouling

Abstract

Tidal stream turbines (TSTs) are crucial for renewable energy generation but face challenges from marine biofouling, significantly impacting their efficiency. Traditional methods for predicting performance and detecting biofouling rely on empirical models and manual inspections, which are often time-consuming and less accurate. This study introduces RegStack, a novel machine learning-based ensemble model, to enhance the prediction of power and thrust coefficients ($C_P$ and $C_T$) and accurately classify biofouling levels in TSTs. Unlike conventional models, RegStack integrates L1 and L2 regularization into a stacking framework, improving robustness, generalization, and interpretability. The model dynamically balances the strengths of multiple regression and classification algorithms, optimizing predictive accuracy while mitigating overfitting. Comprehensive experiments were conducted using an extensive dataset of tidal stream turbine performance metrics under varying operational and environmental conditions. The RegStack model outperformed conventional approaches, achieving a coefficient of determination ($R^2$) of 0.989 for performance predictions, with minimal mean absolute error (MAE) and mean squared error (MSE). Additionally, the model achieved 98.39% classification accuracy, with precision and recall of 0.97, and an F1-score of 0.97 in biofouling detection, demonstrating its effectiveness in real-time turbine health monitoring. By providing an automated, data-driven alternative to traditional methods, this study underscores the potential of advanced machine learning techniques in optimizing TST operations, reducing maintenance costs, and enhancing the reliability of marine renewable energy systems. The proposed RegStack model offers a scalable framework applicable to other renewable energy technologies, supporting sustainable energy advancements.