Using Approximation-Based Global Optimization Algorithm superEGO for Analyzing Wind Energy Potential

Bartłomiej Igliński (), Olgun Aydin and Jarosław Krajewski
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Bartłomiej Igliński: Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7, 87-100 Toruń, Poland
Olgun Aydin: Faculty of Management and Economics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
Jarosław Krajewski: Faculty of Management and Economics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland

Energies, 2025, vol. 18, issue 21, 1-18

Abstract: Recent years have seen a considerable increase in clean, green electricity output from wind energy (WE). It is crucial to obtain the optimum parameters of the two-parameter Weibull distribution (TPWD) for wind speed (WS) to calculate the potential WE. This paper proposes to use the superEGO (SEGO) along with maximum likelihood estimation (MLE) to obtain optimum parameters of the TWPD for WS data. The results showed that SEGO provided better results compared other optimization algorithms used in this context. Moreover, the potential WE for Gdańsk, a city located by the Baltic Sea in northern Poland, was calculated using parameters obtained by using SEGO. It was observed that SEGO performs the best among other optimization algorithms to find optimum parameters for the two-parameter Weibull distribution along with MLE for wind speed.

Keywords: Dividing Rectangles (DIRECT); superEGO (SEGO); Maximum Likelihood Estimation (MLE); wind energy (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: 2025
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