Evaluation of Nine Two-Parameter Probability Distributions for Modeling Wind Speed Data at Three Sits in Western Mountain-Libya

Many studies in the literature have been carried out to find a suitable statistical model in order to describe wind energy potential. Due to the fact that wind speed is the most significant parameter in evaluating the wind energy potential, and because it is a random phenomenon, statistical approaches will be extremely helpful in estimating it. Consequently, probability distributions may be used to estimate wind speed probabilities, which can then be used to forecast wind speed. When estimating the potential wind energy, it is critical to get an accurate calculation of the probability distribution for wind speed data. In this paper nine two-Parameters distributions named Rayleigh (R), Weibull (W), Gumbel (GU), gamma (GA), lognormal (LN), invers Gaussian (IG), normal (NO), logistics (LO) and Nakagami (NA) were employed to ascertain a suitable distribution for wind speed data at three sits Grain, Yafran and Nalut which located in the western mountain – Libya.The goodness of fit tests and the fitted graphics of probability distribution function (PDF) and cumulative distribution function (CDF) have been used to selecting the proper distribution Based on the graphical and the computed goodness of fit results, general inference can be made that Gamma distribution would be the best model for which fitted the data very well for Grain sit, while invers Gaussian distribution was the best model for Nalut sit, finally lognormal distribution was the best model for Yafran sit.