Authors: G. Sudheer Kumar, A. Keerthana
Abstract: Ocean wave energy has great potential for supporting sustainable power production, is continuously available, and has a high energy density, making it one of the most promising renewable energy resources. It is crucial to accurately predict ocean wave properties such as significant wave height (SWH), wave period, and wave energy flow in order to efficiently use wave energy. Prediction accuracy under changing sea conditions is limited by traditional numerical forecasting models' inability to incorporate the ocean's extremely nonlinear, dynamic, and unpredictable character. Through an examination of current developments in ML, DL, ensemble learning, and hybrid prediction models, this research lays forth a framework for intelligent wave energy forecasting that is based on machine learning. The suggested framework takes a look at some of the most popular algorithms for making predictions about the future, such as ANNs, LSTM, CNNs, RF, XGBoost, Transformer-based models, and hybrid ML techniques. These algorithms can predict things like wave height, wave period, and energy generation potential. Hybrid deep learning architectures that include both spatial and temporal feature extraction are more resilient to the unpredictable ocean conditions and provide more accurate forecasts, according to a comparative study. Common metrics used in forecasting for performance assessment include Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R³). In addition, the suggested forecasting framework is fully integrated into a web application built on Django. This web app lets users analyse oceanographic data, run prediction models using machine learning, see the outcomes of the forecasts visually, and keep an eye on the wave energy production via an interactive dashboard. The suggested system is an intelligent decision-support platform that can optimise wave energy converters, manage renewable energy sources, and integrate ocean wave energy into current power systems efficiently
DOI: http://doi.org/10.5281/zenodo.21376151
