LAPSE:2023.35779v1
Published Article
LAPSE:2023.35779v1
Predication of Ocean Wave Height for Ocean Wave Energy Conversion System
Yingjie Cui, Fei Zhang, Zhongxian Chen
May 23, 2023
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
data fitting, ocean wave energy conversion, ocean wave height, predication method
Suggested Citation
Cui Y, Zhang F, Chen Z. Predication of Ocean Wave Height for Ocean Wave Energy Conversion System. (2023). LAPSE:2023.35779v1
Author Affiliations
Cui Y: School of Intelligence Manufacturing, Huanghuai University, Zhumadian 463000, China [ORCID]
Zhang F: School of Intelligence Manufacturing, Huanghuai University, Zhumadian 463000, China
Chen Z: School of Intelligence Manufacturing, Huanghuai University, Zhumadian 463000, China; Henan Key Laboratory of Smart Lighting, Huanghuai University, Zhumadian 463000, China
Journal Name
Energies
Volume
16
Issue
9
First Page
3841
Year
2023
Publication Date
2023-04-29
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16093841, Publication Type: Journal Article
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LAPSE:2023.35779v1
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https://doi.org/10.3390/en16093841
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May 23, 2023
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CC BY 4.0
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[v1] (Original Submission)
May 23, 2023
 
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Calvin Tsay
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