Authors: Assistant Professor Seemala Gouthami, P.Rama Krishna
Abstract: The growing demand for clean and sustainable energy has increased the importance of ocean wave energy as a reliable renewable power source. Efficient utilization of this resource depends on accurate forecasting of wave characteristics, which directly influences energy conversion efficiency, operational planning, and power grid stability. Conventional numerical forecasting models often struggle to represent the highly dynamic and nonlinear behavior of ocean waves, resulting in reduced prediction accuracy under changing environmental conditions. This paper presents a comprehensive study of modern Machine Learning (ML) techniques for wave energy forecasting, emphasizing the application of Deep Learning (DL) architectures, ensemble learning methods, and hybrid prediction models. Various learning approaches, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Random Forest (RF), XGBoost, and transformer-based models, are analyzed for their ability to estimate significant wave height (SWH), wave period, and wave energy potential. The study also examines hybrid frameworks that integrate physical oceanographic models with data-driven learning techniques to improve forecasting reliability under complex marine conditions. Comparative evaluation demonstrates that hybrid deep learning models generally achieve better prediction accuracy, lower forecasting error, and greater robustness than conventional machine learning approaches. Furthermore, recent developments in transformer networks, Generative Adversarial Networks (GANs), and real-time data assimilation are discussed as promising directions for improving future wave energy prediction systems. The findings indicate that intelligent forecasting frameworks can significantly enhance renewable energy management, optimize wave energy converter performance, and support efficient integration of ocean energy into modern power systems.
