Time-Variations of Wave-Energy and Forecasting Power Availability using Different Techniques

Wave energy has captured the attention of island nations that have a significantly larger sea area compared to the land area. An accurate forecasting model is required to correctly predict the availability of wave energy with all the required variables clearly defined. In this work, wave energy forecasting models: artificial neural network, regression and time series techniques were developed using wave height and wave period data. The models were tested using the wave data measured near Muani, Kadavu in Fiji for 14 months. The performance of each model is assessed using mean squared error, root mean square error, mean absolute error and coefficient of determination. The empirical results reveal that the artificial neural network model is more efficient and accurate in forecasting wave energy in comparison to the regression and time series models