Due to the increasing environmental and economic cost of fossil fuels, alternative sources of energy are needed. One of these sources is wind energy. The wind-turbines extract kinetic energy from the wind to convert it to mechanical energy and then transfer to electrical energy. Wind speed is the most important parameter for an efficient wind energy system. In this work the Microsoft excel software used to analysis of wind speed data and evaluate the wind speed distribution. the wind speed probability estimated and analyzed by using five methods of Weibull and Rayleigh distributions and evaluated the best methods to represent the actual data based on monthly mean wind speed data of the Ma'an city site, Jordan. furthermore, from the analysis, it has been found that the energy pattern factor method EPFM is the best method to represent the actual data and the EPFM is the best and most accurate and efficient method to determine the Weibull distribution parameters (k) and (c). In addition, in this work, the annual average shape parameter (k) is 3.4 and the annual average scale parameter (c) is 4.0 m/s. The most probable wind speed is 4.4 m/s in August and the maximum wind speed carrying maximum energy is 5.2 m/s occurs in October. Meanwhile, the maximum power and energy density are 57.5 W/m2, 42.8 kWh/m2 respectively in August. Moreover, the site has annual power density 39.3W/m2 and 345.5 kWh/m2 of energy density. Keywords: Renewable energy, Wind energy, Wind speed, Weibull distributions, Power density, Energy density DOI: 10.7176/JETP/11-4-05 Publication date:September 30th 202
