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

Xiang Liao, Kai Wei, Qingshan Yang
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Abstract

Ocean current forecasting is essential for tidal renewable energy generation and operation. However, comprehensive studies and an efficient approach for forecasting the current direction at multiple points along the water depth are still lacking. 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 were measured by a seabed-mounted acoustic Doppler current profiler (ADCP) in the Haitian Strait and 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 predication and multistep ahead prediction were also examined.
利用经验模式分解和翘曲高斯过程混合模型预测水流方向的数据驱动方法
洋流预报对潮汐可再生能源的发电和运行至关重要。然而,目前仍缺乏全面的研究和有效的方法来预测沿水深多点的海流方向。本研究开发了一种数据驱动的方法,以在合理量化不确定性的情况下实现海流方向的短期预测。所开发的方法在预测过程中采用了经验模式分解(EMD)和翘曲高斯过程(WGP)。海流数据是由安装在海底的声学多普勒海流剖面仪(ADCP)在海地海峡测量的,用于说明所开发的方法。测量的海流方向数据通过平均移动法进行预处理,以获得主成分和随机成分,从而提高预报精度。随机分量被分解为固有模式函数(IMFs)和残差。然后用 WGP 方法预测当前方向的主成分、IMF 和残差。通过与单一标准 GP、单一 WGP 和 EMD+GP 模型的比较,研究了所开发方法的预测性能。研究了核函数和训练输入对预测效率和精度的影响。此外,还考察了所建模型在单步预测和多步超前预测中的外推性能。
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