Improving extreme offshore wind speed prediction by using deconvolution

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.

Automated summary

This study proposes an innovative method for predicting extreme values in offshore engineering by considering environmental loads and structural reliability issues. The method differs from traditional approaches by not assuming any specific extrapolation function and relying on the intrinsic qualities of the data set. It has been demonstrated to be effective through the analysis of two wind speed data sets and comparison with the Naess-Gaidai method.