Modeling, prediction, and optimization of hybrid oscillating water column-Piezoelectric wave energy converter device using machine learning models

This study focuses on optimizing the geometric parameters of a hybrid wave energy converter (WEC) that integrates an oscillating water column (OWC) device with a piezoelectric plate wave energy converter (PWEC). The hydrodynamic response and corresponding power output are computed using a constant-panel boundary element method, and a high-fidelity database is generated through Latin hypercube sampling. Five data-driven models—Random Forest, XGBoost, LightGBM, CatBoost, and a multilayer perceptron—are evaluated, with XGBoost exhibiting the highest predictive accuracy ( R 2 = 0.9959 , MAPE = 0.02 % , RMSE = 61.86 ). Feature ranking using Spearman correlation and SHAP values identifies l 1 / h, r / h , and d 1 / h as the dominant contributors to total generated power. Interpretable machine-learning tools, particularly accumulated local effects (ALE), are used to locate high-performance regions in the input space, which subsequently guide a response surface methodology (RSM) search. The combined ML–RSM framework yields an optimal configuration of l 1 / h = 0.21 , d 1 / h = − 0.08 , g p / h = 0.06 , d 2 / h = − 0.19 , and r / h = 0.63 , resulting in a maximum power output of 7912 Wm − 1 . This represents an improvement of approximately 23% over the maximum value present in the training dataset. This study represents a significant advancement in optimizing the total power output of hybrid WEC systems. The results demonstrate that machine-learning-assisted optimization offers an efficient and accurate pathway for enhancing overall device performance.