Harnessing Wave Energy: A Regression Model Approach to Power Prediction

Wave energy converters (WECs) are now considered viable renewable energy sources since they are affordable and sustainable. It is critical to precisely predict the overall power generation of WECs for practical usage and integration into the electrical grid. In this study, a machine learning model is created and trained to predict the overall power generation of WECs using the WEC data set. The study’s objective is to evaluate how accurately machine learning techniques predict the power output of WECs. The proposed approach utilizes a neural network architecture trained using the WEC data set to determine the power output of a WEC. The findings of this study aid in the development of more accurate and reliable models and provide helpful information on the usage of machine learning algorithms for WEC power forecasts. The suggested technique could simplify forecasting the overall power of WECs as a renewable energy source in practical applications.