A Novel Machine Learning Approach for the Prediction of Optimized Energy Output on Large-Scale Wave Energy Farms

Fossil fuels have been the backbone of industry and society but have gotten increasingly unreliable and unsustainable over time. While traditional methods of generating renewable energy exist and are increasing in viability and prevalence, wave energy has emerged as a promising source of renewable energy, with numerous prototypes of small wave energy farms constructed across coastlines. However, with the expensive costs currently associated with energy output calculations, organizations face difficulties determining wave farm energy output. Machine learning offers valuable insight into optimizing energy within wave energy farms by leveraging coordinate points, allowing a feedforward neural network to predict energy output accurately. Ultimately, this research aims to support clean energy organizations and other businesses in efficiently managing and upscaling wave energy farms, boost adaptation and advancements of wave and reliable energy, and continue sustainable development.