An attention-based deep learning model for phase-resolved wave prediction

Abstract

Phase-resolved wave prediction capability, even if only over two wave periods in advance, is of value for optimal control of wave energy converters (WECs), resulting in a dramatic increase in power generation efficiency. Previous studies on wave-by-wave predictions have shown that an Artificial Neural Network (ANN) model can outperform the traditional linear wave theory-based model in terms of both prediction accuracy and prediction horizon when using synthetic wave data. However, the prediction performance of ANN models is significantly reduced by the varying wave conditions and buoy positions that occur in the field. To overcome these limitations, a novel wave prediction method is developed based on the neural network with an attention mechanism. This study validates the new model using wave data measured at sea. The model utilizes past time histories of three Sofar Spotter wave buoys at upwave locations to predict the vertical motion of a Datawell Waverider-4 at a downwave location. The results show that the attention-based neural network model is capable of capturing the slow variation in the displacement of the buoys, which reduces the prediction error compared to a standard ANN and Long Short-Term Memory (LSTM) model.