Predicting the Heave Displacement of a Nonbuoyant Wave Energy Converter Using Tree-Based Ensemble Machine Learning Models

Scientists have introduced new methods for capturing energy from ocean waves. Specifically, scientists have focused on a type of wave energy converter (WEC) that is nonbuoyant (i.e., a body that cannot float). Typically, the WEC is most effective when it is in resonance, which occurs when the natural frequency of the WEC aligns with that of the ocean waves. Therefore, accurately predicting the movement of the WEC is crucial for adjusting its system to resonate with the incoming waves for optimal performance. In this study, artificial intelligence techniques, such as random forest, extra trees (ET), and support vector machines, are created to forecast the vertical movement of a nonbuoyant WEC. The developed models require two variables as input, namely, the water wave height and its time period. A total of approximately 4500 data points, which include nonlinear water wave height and duration obtained from a laboratory experiment, are used as the input for these models, with the resulting vertical movement as the output. When comparing the three models based on their processing speed and accuracy, the ET model stands out as the most efficient. Ultimately, the ET model is tested using data from a real ocean setting.