Information about the wind power df(density function) is very important when measuring the wind energy potential for a specific area. Usually, the wind power df provides knowledge about the mean power, which is an indicator of the energy potential. However, the mean power does not describe well the characteristics of power density. Thus, by knowing information about other statistical properties, such as standard deviation, skewness and kurtosis, better insight about the characteristics and properties of power density can be obtained. This study proposes a method to derive a wind power density model and its statistical properties particularly from well-known dfs, namely, the Weibull, Gamma and Inverse Gamma dfs. Applying the method of transformation and Monte Carlo integration has been discussed to address the difficulty of finding the different statistical properties of power density. In addition, an application of the proposed method is demonstrated by a case study that involves wind speed data from several stations in Malaysia. (C) 2015 Elsevier Ltd. All rights reserved.