Abstract
Accurate tidal current prediction plays a critical role with increasing utilization of tidal energy. The classical prediction approach of the tidal current velocity adopts the harmonic analysis (HA) method. The performance of the HA approach is not ideal to predict the high frequency components of tidal currents due to the lack of capability processing the non-astronomic factor. Recently, machine learning algorithms have been applied to process the non-astronomic factor in the prediction of tidal current. In this paper, a tidal current velocity prediction considering the effect of the multi-layer current velocity method is proposed. The proposed method adopts three machine learning algorithms to establish the prediction models for comparative investigations, namely long-short term memory (LSTM), back-propagation (BP) neural network, and the Elman regression network. In the case study, the tidal current data collected from the real ocean environment were used to validate the proposed method. The results show that the proposed method combined with the LSTM algorithm had higher accuracy than both the commercial tidal prediction tool (UTide) and the other two algorithms. This paper presents a novel tidal current velocity prediction considering the effect of the multi-layer current velocity method, which improves the accuracy of the power flow prediction and contributes to the research in the field of tidal current velocity prediction and the capture of tidal energy.
Highlights
Tidal currents take place simultaneously with the rise and fall of the tide
A tidal current velocity prediction considering the effect of the multi-layer current velocity prediction method is proposed, which enables the effect of turbulence flow to be reduced in prediction accuracy; The long-short term memory (LSTM) algorithm method is applied in tidal current prediction; and Comparative investigations of machine learning based approaches on tidal current prediction are given
Four types of algorithms were adopted to compare the performance in tidal current prediction
Summary
Tidal currents take place simultaneously with the rise and fall of the tide. The vertical motion of tides leads the water to move horizontally, which generates tidal currents. In terms of the tidal current prediction method, there are two general types of methods, namely the harmonic analysis method and machine learning based approach. Artificial neural network (ANN) is a type of classical machine learning method [19,20,21,22]. The LSTM algorithm is a kind of special recurrent neural network (RNN), which is more suitable to process long-term dependence problems [29]. This feature of the LSTM algorithm makes it suitable to be applied in tidal current prediction because the main component of tidal currents is a kind of sequential periodic wave
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