## 1 Introduction
### Related Literature on Wind Potential Assessment
Using the KSS, root mean square error (RMS), and chi-square (CHI) tests, they found that the Mixture Weibull method performed best overall, closely followed by LGN. The WEI and GAM distributions were less accurate, emphasizing the need to fit multiple distributions to find the most appropriate one, as the accuracy of a distribution in describing wind speed is site-specific. In a study conducted by [30], along Turkey's Aegean coast, various distributions, including RAY, inverse Weibull (IW), Burr type III, extreme value (EV), GAM, inverse gamma (IG), Marshall-Olkin extended Lindley (MOEL), GEV, and EW, were analyzed for wind speed modeling. The EW distribution provided the best overall fit, followed by the GEV distribution. The WEI, EV, GAM, and MOEL distributions performed well in cases where skewness was less than one and kurtosis was below four. In contrast, the Burr type III, IW, and IG distributions were more effective for data with higher skewness and kurtosis values. RAY had the worst performance. Also, ref. [31] analyzed ten years of wind speed data from four stations in Johor, Malaysia, using the GAM, GEV, LGN, RAY, and WEI distributions. The GEV distribution provided the best fit for the Senai, Mersing, and Batu Pahat stations, while the GAM distribution was most suitable for Kluang. In another study, ref. [32] assessed wind speed at ten sites in Tamil Nadu, India, using 39 years of historical data. They utilized nine distribution models, including exponential, GAM, GEV, IG, Kumaraswamy, LL, LGN, Nakagami, and WEI, to evaluate the wind characteristics. The study found that the GEV distribution was the most suitable for most stations, with Kumaraswamy also performing well, particularly for negatively skewed data. This highlighted the need to select different distributions based on varying topographical conditions. Similarly, ref. [33] evaluated wind speed at the Mersing station in Malaysia, comparing the GAM, GEV, LGN, RAY, and WEI distributions using the maximum likelihood method (MLM). Consistent with the findings of [32], the GEV distribution emerged as the best fit in this study, underscoring GEV's robustness in diverse geographical contexts. In South Africa, ref. [34] assessed the wind energy potential in Mthatha, in Eastern Cape Province, using two-parameter and three-parameter Weibull distribution models. Their study utilized wind data collected at a 10 m height above ground level from 2018 to 2023. The analysis revealed low average wind speeds of 3.30 m/s and a wind power density of approximately 48.48 W/m\({}^{2}\). These findings indicate that the area may not be ideal for large-scale wind energy projects but is suitable for standalone applications. The prevailing wind directions were predominantly from the south and east-southeast. Similarly, ref. [10] evaluated the wind potential of Fort Beaufort, South Africa, using the two-parameter Weibull distribution. They applied eight numerical methods to determine the Weibull scale and shape parameters based on five-and-a-half years of hourly wind data. The study concluded that Fort Beaufort is suitable for small-scale wind energy projectswith prevailing southeast winds. The authors also recommended the use of augmented wind turbines incorporating concentrators and diffusers to improve efficiency in areas with low wind speeds. The WEI distribution has been shown to be highly effective in several studies. For example, ref. [20] investigated the wind speed distribution in Agadir, Morocco, comparing the WEI, RAY, GAM, and LGN distributions. Their results, validated by goodness-of-fit tests, identified the WEI distribution as the most accurate model for estimating the annual wind power density. Consequently, they recommended the WEI distribution for wind energy assessments in the region. Similarly, ref. [35] analyzed wind speed data from Tarnab, Peshawar (2004-2023) using the WEI and RAY distributions. Their findings revealed that the WEI distribution closely matched the observed data, further affirming its superiority for wind energy analysis.