## 1 Introduction
### Related Literature on Wind Potential Assessment
In Jordan [36], the wind energy potential across nine locations was assessed using WEI, RAY, and GAM distributions. Among the sites, King Hussein Airport exhibited the highest wind power density, with the WEI distribution again providing the best fit. Ref. [37] evaluated the wind energy potential in Omu Aran, Nigeria, over five years using the WEI and RAY distributions. Their analysis reported a mean yearly wind speed of 3.964 m/s, categorizing the location in wind power class 1. The WEI distribution demonstrated strong performance, making it suitable for preliminary wind power plant design in Kwara State. This study further highlighted the reliability of the WEI distribution for wind energy assessments. Studies in Pakistan by [38; 39] demonstrated the reliability of the WEI distribution in wind energy assessments. Ref. [38] assessed the wind energy potential in Jhimpir, Sindh, Pakistan, using eight probability distribution functions: WEI, RAY, GUM, LGN, Logistic (LOG), GAM, generalized Lindley (GL), and Cauchy distributions (CD). Based on 3 years and 6 months of wind speed data, their analysis identified the WEI and RAY distributions as the best-fitting models, with the WEI distribution showing slightly better performance. Wind power density estimates ranged from 84.67 to 698.65 W/m\({}^{2}\), indicating that the site is suitable for wind power production. The predominant wind directions were from the east and southeast. Similarly, ref. [39] assessed the wind power potential in Pakistan's Sindh, Balochistan, and Khyber Pakhtunkhwa Provinces, utilizing the WEI distribution. On the other hand, ref. [40] assessed the wind energy potential in southwestern Aceh coastal areas, Indonesia, using WEI and MLM. The wind power densities obtained ranged from 4590 to 26,040 W/m\({}^{2}\), classifying the site as low-class for wind power generation. Most studies assessing the wind potential in the Eastern Cape Region of South Africa have predominantly relied on the two-parameter Weibull distribution. There has been limited exploration of other distributions. When alternatives have been considered, they have typically focused only on fitting without leveraging third-moment statistical properties to evaluate wind power densities or obtaining additional statistical measures such as mean, variance, skewness, and kurtosis from the determined distribution parameters. This study seeks to address this gap by applying multiple distributions, such as the Weibull (WEI), Rayleigh (RAY), gamma (GAM), generalized extreme value (GEV), inverse Gaussian (IGA), and Gumbel (GUM) methods, to determine the most suitable distribution for the location and to assess the statistical properties and wind power density of the area. Section 2 details the materials and methods used in this research, including a description of the site, its location, the source of the wind data, and an overview of the distribution models and numerical methods for parameter estimation. It also covers the test statistics used for the goodness-of-fit analysis. Section 3 presents and discusses the main results of the study, while Section 4 concludes with a summary of the findings and recommendations.