Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity

Bo Feng, Peng Qian, Yulin Si, Xiaodong Liu, Haixiao Yang, Huisheng Wen and Dahai Zhang
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Bo Feng: Ocean College, Zhejiang University, Hangzhou 310000, China
Peng Qian: Ocean College, Zhejiang University, Hangzhou 310000, China
Yulin Si: Ocean College, Zhejiang University, Hangzhou 310000, China
Xiaodong Liu: Ocean College, Zhejiang University, Hangzhou 310000, China
Haixiao Yang: Ocean College, Zhejiang University, Hangzhou 310000, China
Huisheng Wen: Ocean College, Zhejiang University, Hangzhou 310000, China
Dahai Zhang: Ocean College, Zhejiang University, Hangzhou 310000, China

Energies, 2020, vol. 13, issue 23, 1-19

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.

Keywords: tidal current prediction; multilayer current velocity; UTide; machine learning; turbulence flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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