## 4 Conclusions  
This study evaluated the wind energy potential at Fort Hare in the Eastern Cape, South Africa, using six statistical distribution models. It emphasizes the importance of selecting accurate models for wind energy estimation, moving beyond the traditional reliance on the two-parameter Weibull distribution. While the Weibull model is popular for its simplicity and flexibility, this study stresses the need for models tailored to a site's specific wind characteristics, which can vary over time and location.  
A goodness-of-fit test approach was used, normalizing and averaging three statistical indicators to calculate the total error and rank model performance. This method, adapted from [45], was applied for the first time in wind potential assessments in the Eastern Cape. The comparison revealed that the generalized extreme value (GEV) distribution outperformed all the other models, accurately capturing wind speed variability and extremes, with the lowest total error being 0.020. In contrast, the Weibull and Rayleigh distributions recorded higher total errors of 0.5421 and 0.667, respectively, highlighting their limitations in representing highly variable wind data with extreme speeds. The Gumbel model also showed strong performance, confirming its applicability in similar environments. The GEV model was particularly effective in capturing seasonal wind speed variations and demonstrated consistent reliability across multiple goodness-of-fit indicators. These results highlight the importance of advanced statistical techniques in ensuring accurate wind power density estimations. Using the third moment for calculating wind power densities in models with more than two parameters could also improve accuracy.  
The study classifies the site as having insufficient wind energy potential for large-scale electricity generation but recommends small-scale or augmented wind turbines for standalone systems. Additionally, turbines should be strategically placed in the southeast direction to maximize wind energy utilization, in alignment with prevailing wind patterns. These insights provide valuable guidance for optimizing wind resource utilization in South Africa and supporting sustainable energy development in areas with moderate wind potential.  
While this study provides valuable insights into the wind energy potential at Fort Hare University, future research should focus on assessing wind characteristics at different heights above ground level, which is crucial for optimal wind turbine placement. Additionally, alternative methods such as Bayesian inference or metaheuristic algorithms could further enhance the accuracy of parameter estimation. Furthermore, future studies should integrate wind turbine power curves, techno-economic assessments, and advanced optimization techniques to improve wind energy potential assessments.  
Conceptualization, methodology, software, formal analysis, and writing--original draft preparation, N.S.; writing--review and editing, methodology, formal analysis, supervision, and funding acquisition P.M. and G.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** Special acknowledgement is given to the National Research Foundation (NRF), Research Niche Area: Renewable Energy--Wind of GMRDC, University of Fort Hare for their financial support. The authors express their gratitude to South African Weather Services (SAWS) for providing data for this research at no cost.  
**Conflicts of Interest:** The authors declare no conflicts of interest.