Improving extreme offshore wind speed prediction by using deconvolution

被引：29

作者：

Gaidai, Oleg ^{[1
]}

Xing, Yihan ^{[2
]}

Balakrishna, Rajiv ^{[2
]}

Xu, Jingxiang ^{[1
]}

机构：

[1] Shanghai Ocean Univ, Coll Engn Sci, Shanghai Engn Res Ctr Hadal Sci & Technol, Shanghai, Peoples R China

[2] Univ Stavanger, Stavanger, Norway

来源：

HELIYON | 2023年 / 9卷 / 02期

关键词：

Extreme wind speed estimation; Convolution; Reliability; Measured wind speed data; Offshore wind; DISTRIBUTIONS; WEIBULL;

D O I：

10.1016/j.heliyon.2023.e13533

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently con-structed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.

引用

页数：9

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