A Data-Driven Approach for Forecasting Current Direction With a Hybrid Model of Empirical Mode Decomposition and Warped Gaussian Process

Abstract

Ocean current forecasting is essential for tidal renewable energy generation and operation. However, forecasting the current direction at multiple points along the water depth are still lacking of comprehensive studies. In this study, a data-driven approach was developed to attain short-term prediction in the current direction with reasonable uncertainty quantification. The developed approach employed empirical mode decomposition (EMD) and the warped Gaussian process (WGP) in the forecasting process. The ocean current data, which were measured by a seabed-mounted acoustic Doppler current profiler in the Haitian Strait, were used to illustrate the developed approach. The measured current direction data were preprocessed with the average shifting method to obtain the principal and random components for the improvement of the forecasting accuracy. The random components were decomposed into intrinsic mode functions (IMFs) and residuals. The principal components, IMFs, and residuals of the current direction were then forecasted by the WGP approach. The forecasting performance of the developed approach was investigated through comparisons with those of single standard GP, single WGP, and EMD + GP models. The effects of the kernel function and training input on the forecasting efficiency and precision were investigated. The extrapolation performances of the proposed model for a 1-step prediction and multistep-ahead prediction were also examined.