Modeling of Wind Energy Potential in Marmara Region Using Different Statistical Distributions and Genetic Algorithms

Abstract

many distribution functions for representing the wind power potential have been proposed. The fitness of the results mainly depends on the used estimation method and the wind pattern of the analyzed area. The selection of a convenient statistical distribution for characterizing wind speed distribution is a critical factor. This paper utilizes three well-known statistical distributions, namely, Weibull, Poisson, and Lognormal to model the wind power in Catalca in the Marmara area located in Turkey. The parameters of these distributions are optimized based on the Genetic Algorithms optimization. The real data of Catalca which was obtained from the national metrology station for three years, are statistically analyzed at 30, 60, and 80 m heights. Root mean square error, correlation coefficient, and mean absolute error measures are exploited to show distributions accuracy differences. Based on the obtained results, the Weibull distribution is superior to others in modelling the real data of Catalca in terms of all used accuracy measures.