In this paper, five probability distribution functions are employed to fit the wind speed data from four different geographical locations in the world in a preliminary analysis. These wind regimes are selected such that they represent wide ranges of mean wind speeds and present different shapes of wind speed histograms. The wind speed data used for modelling consist of 10-min average SCADA data from three US wind farms and hourly averages recorded at a weather station in Canada. Out of the five, three functions, namely Weibull, Rayleigh, and gamma, which provide a better fit to the data, are selected to carry out further analyses. This study investigates the ability of these functions to match different statistical descriptions of wind regimes. Parameter estimation is done by the method of moments, and models are evaluated by root mean square error and R square methods. The suitability of PDFs to predict the wind power densities and annual energy production using manufacturers' power curve data at three of the selected sites is analysed. Power curves extracted from actual data of one wind farm using novel four- and five-parameter logistic approximations are also introduced here for energy analyses.