Predication of Ocean Wave Height for Ocean Wave Energy Conversion System

Yingjie Cui, Fei Zhang () and Zhongxian Chen
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Yingjie Cui: School of Intelligence Manufacturing, Huanghuai University, Zhumadian 463000, China
Fei Zhang: School of Intelligence Manufacturing, Huanghuai University, Zhumadian 463000, China
Zhongxian Chen: School of Intelligence Manufacturing, Huanghuai University, Zhumadian 463000, China

Energies, 2023, vol. 16, issue 9, 1-13

Abstract: Ocean wave height is one of the critical factors to decide the efficiency of the ocean wave energy conversion system. Usually, only when the resonate occurs between the ocean wave height (ocean wave speed in the vertical direction) and ocean wave energy conversion system, can the conversion efficiency from ocean wave energy into electric energy be maximized. Therefore, this paper proposes two predication methods to predict the future ocean wave height in 1.5–2.5 s. Firstly, the data fitting of real ocean wave height is achieved by the polynomial method, which is beneficial to the predication of ocean wave height. Secondly, the models of the moving average (MA) predication method and auto regressive (AR) predication method are presented by the time series analysis process. Lastly, after the predication of ocean wave height by the MA method and AR method, and compared with the data fitting result of real ocean wave height, it can be found that the AR method is more accurate for the predication of ocean wave height. In addition, the predication results also indicated that the error between the predication value and true value in the future 2.5 s is considered acceptable, which provides enough time to optimize the operation process of the ocean wave energy conversion system by a suitable control method.

Keywords: ocean wave height; data fitting; predication method; ocean wave energy conversion (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: 2023
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