In calculating the wind energy potential of a region, some important points such as determining the distribution used to model wind speeds and estimating the parameters of the distribution accurately should be considered. Many different distributions have been proposed in wind energy literature over the years. In this paper, some of these studies are reviewed. Then, Inverted Kumaraswamy (IKum) distribution is used for the first time to model wind speed data as an alternative to the well-accepted Weibull distribution. Maximum Likelihood, Least Squares, and Maximum Product of Spacing methodologies are employed in estimating the parameters of the IKum distribution. A Monte Carlo simulation study is conducted for comparing the efficiencies of these methods. The wind speed data sets considered in this study include wind speeds from 6 stations located around Lake Van in Turkey. Modeling performances of the Weibull and IKum distributions are evaluated with the well-known goodness-of-fit criteria and power density error values. Results show that the IKum distribution can be considered as an alternative to the well-accepted Weibull distribution.

在计算区域的风能潜力时，应考虑一些重要事项，例如确定用于模拟风速的分布以及准确估算分布的参数。多年来，在风能文献中已经提出了许多不同的分布。本文对其中一些研究进行了综述。然后，首次使用反向Kumaraswamy（IKum）分布对风速数据进行建模，以替代公认的Weibull分布。在估计IKum分布的参数时，采用了最大似然，最小二乘和最大乘积方法。进行了蒙特卡罗模拟研究，以比较这些方法的效率。本研究中考虑的风速数据集包括位于土耳其范湖周围6个站的风速。使用众所周知的拟合优度标准和功率密度误差值来评估Weibull和IKum分布的建模性能。结果表明，IKum分布可以视为公认的Weibull分布的替代方法。