Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity
| Autor: | Haixiao Yang, Dahai Zhang, Yulin Si, Xiaodong Liu, Feng Bo, Huisheng Wen, Peng Qian |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
UTide
Control and Optimization multilayer current velocity 010504 meteorology & atmospheric sciences Field (physics) Computer science 020209 energy Energy Engineering and Power Technology Ocean environment 02 engineering and technology 01 natural sciences lcsh:Technology GeneralLiterature_MISCELLANEOUS Harmonic analysis 0202 electrical engineering electronic engineering information engineering turbulence flow Electrical and Electronic Engineering Engineering (miscellaneous) 0105 earth and related environmental sciences tidal current prediction Renewable Energy Sustainability and the Environment business.industry Turbulence lcsh:T Process (computing) Tidal current machine learning Astrophysics::Earth and Planetary Astrophysics business Tidal power Energy (miscellaneous) Marine engineering |
| Zdroj: | Energies; Volume 13; Issue 23; Pages: 6417 Energies, Vol 13, Iss 6417, p 6417 (2020) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13236417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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