Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models

Abstract

The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter's duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location.