Wave Parameters Prediction for Wave Energy Converter Site using Long Short-Term Memory

Forecasting the behaviour of various wave parameters is crucial for the safety of maritime operations as well as for optimal operations of wave energy converter (WEC) sites. For coastal WEC sites, the wave parameters of interest are significant wave height (Hs) and peak wave period (Tp). Numerical and statistical modeling, along with machine and deep learning models, have been applied to predict these parameters for the short and long-term future. For near-future prediction of Hs and Tp, this study investigates the possibility of optimally training a Long Short-Term Memory (LSTM) model on historical values of Hs and Tp only. Additionally, the study investigates the minimum amount of training data required to predict these parameters with acceptable accuracy. The Root Mean Square Error (RMSE) measure is used to evaluate the prediction ability of the model. As a result, it is identified that LSTM can effectively predict Hs and Tp given their historical values only. For Hs, it is identified that a 4-year dataset, 20 historical inputs, and a batch size of 256 produce the best results for three, six, twelve, and twenty-four-hour prediction windows at half-hourly step. It is also established that the future values of Tp can be optimally predicted using a 2-year dataset, 10 historical inputs, and a 128-batch size. However, due to the much dynamic nature of the peak wave period, it is discovered that the LSTM model yielded relatively low prediction accuracy as compared to Hs © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.