Short-term Wave Forecasting using Gaussian Process for Optimal Control of Wave Energy Converters

Abstract

Wave forecasting is an important prerequisite for implementing optimal control of wave energy converters since the control of power take-off problem is dependent on a reference strategy that must be calculated from a time-varying wave excitation force value. The real-time computation of the wave force excitation is a considerable challenge, depending on predicted values of wave elevation and short-term frequency. In this study, the performance of a Gaussian Process (GP) procedure for short-term wave forecasting is investigated and compared with Neural Network (NN) and Autoregressive modelling (AR) methods. The GP strategy is not only capable of forecasting the mean value of wave elevations but also provides the uncertainty of forecasting, which is beneficial to the safe operation, robust and optimal control of the wave energy converter devices. According to the non-linear and stochastic nature of wave motion, 3 different covariance functions including spectral mixture (SM), quasi-periodic spectral mixture (QPSM) and Matérn (MA) periodic covariance functions are adopted and analyzed in the work. Results indicate the feasibility of using the GP approach in a real application from the viewpoints of high forecasting accuracy and acceptable computational complexity. The forecasting performance comparisons between the AR and NN models consider two dissimilar real wave validation datasets. The overall outcome is that the GP outperforms its counterparts. However, prediction of the uncertainty is a strong contribution compared with other approaches.