Evaluation of genetic algorithm alternatives for wind speed modeling using grey relational analysis

Abstract Wind speed modeling is a crucial tool for the use of sustainable energy by reducing fossil fuel dependence. This implies energy efficiency of a wind turbine and the assessment of wind energy potential for renewable energy development. Weibull distribution is commonly used in wind speed modeling due to its flexibility and effectiveness and to determine wind speed patterns. Therefore, this paper focuses on parameter estimates of the Weibull distribution using genetic algorithm (GA) optimization based on the maximum likelihood (ML) method. This study addresses the evaluation of the different fitness functions and the selection of different GA parameter sets, including population size, crossover rate, and mutation rate. The proposed fitness function in GA provides to estimate the shape parameter of the Weibull distribution. The proposed alternatives to the GA method are evaluated based on Kolmogorov–Smirnov (KS), coefficient of determination (R 2 ), root mean square error (RMSE), Akaike information criterion, Bayesian information criterion, and power density error (PDE) over three different wind speed datasets. The grey relational analysis is used in ranking the GA alternatives. The best GA alternative is also compared to particle swarm optimization based on maximum likelihood estimation and provides satisfactory results based on R 2 , RMSE, and PDE. A simulation study is performed to evaluate the performances of GA alternatives with respect to deficiency criterion. Finally, we recommend the GA 1 and GA 3 alternatives for Weibull parameter estimation; these alternatives contribute to the selection of GA parameter sets in practice.