## 1 Introduction
### Related Literature on Wind Potential Assessment  
Wind data are often treated as a continuous random variable, requiring continuous probability distributions for modeling predictable wind patterns [19]. Among these, the two-parameter Weibull (WEI) distribution is widely used, though its performance varies depending on location and wind regimes [20; 21]. Studies often explore alternative distributions to determine the most suitable model for specific regions. For instance, ref. [22] investigated the wind potential across nine Nigerian locations, identifying the Rayleigh (RAY) distribution as the best fit for six sites, while the log-normal (LGN) distribution was superior for Maiduguri, Abuja, and Akure. Similarly, ref. [23] analyzed wind speed characteristics using both commonly used distributions (Generalized Rayleigh (GR), GAM, GUM, Exponentiated Weibull (EW)) and more recent ones (Exponentiated Half-Logistic (EHL), Exponentiated Half-Normal (EHN), Skew Logistic (SL), and Generalized Extreme Value (GEV)) at seven meteorological stations in northern Iran. The study found that the EHN distribution provided the best overall fit. A study conducted by [24] assessed 44 wind speed distribution models in Lebanon, finding the Wakeby (WAK) and Beta distributions to be generally the best. The WAK distribution provided the best fit for Younine, Birket Aarous, Mqaybleh, and Hekr El Dahri, while the Beta distribution was the best fit for Khiam, Iskandarounah, Khartoum, and Qaraoun. Gumbel Min and generalized logistic were best for Ain ed Dabaa and Ras Ouadi Ed Darje, respectively. Likewise, ref. [25] assessed various wind speed distribution models in Malaysia using goodness-of-fit criteria like the coefficient of determination (COD), Kolmogorov-Smirnov statistic (KSS), [PERSON]'s information criterion (AIC), and skewness/kurtosis deviation (SKDEV). Eight distributions, including LGN, WEI, RAY, exponential, Burr, GAM, inverse Gaussian (IGA), and inverse gamma, were tested on data from Kuantan and Balok Baru. The GAM distribution was found to be the best fit for wind speed data at both locations. Ref. [26] evaluated the wind power potential at eight sites in northern Cyprus using wind data collected from Lefkosa. Their analysis revealed that the GEV distribution provided the best fit for Lefkosa, Ercan, Girne, Guzelyurt, and Dipkarpaz. In contrast, the log-logistic (LL), WEI, and GAM distributions were more accurate for Gazimagusa, YeniBogazici, and Salamis, respectively. The RAY distribution, however, performed poorly across all sites. The study reported wind power densities ranging from 38.76 to 134.29 W/m\({}^{2}\), suggesting limited potential for large-scale energy production. Nevertheless, the 5 kW Aeolos-V2 wind turbine was identified as suitable for meeting household energy needs in the region. Also, ref. [27] conducted wind potential assessment in the Marmara Region, Turkey, using ten distributions. The GAM, Burr, and WEI distributions were found to be superior, with metaheuristic methods enhancing parameter estimation accuracy. Ref. [28] evaluated onshore wind potential in seven southern Iranian coastal cities to reduce fossil fuel reliance. They analyzed six probability distributions, finding the WEI suitable for one station, GEV for three, and GAM for the rest. The study recommended the GAM distribution for its simplicity and effectiveness in assessing the region's wind energy potential. These studies from different countries confirm that the performance of wind speed distributions is site-specific. Furthermore, the research presented in [19] reviewed fourteen probability distributions for wind speed data at seventeen Indian locations. The study found that the WEI distribution often struggled with heterogeneous data. The truncated normal-gamma distribution fit best at four locations, while the GAM distribution was most suitable at six locations among the two-parameter distributions. In contrast, the study by [29] assessed wind speed characterization at twelve Nigerian stations, comparing five distribution methods.