Assessing the Wind Energy Potential: A Case Study in Fort Hare, South Africa, Using Six Statistical Distribution Models

Wind energy is a clean, inexhaustible resource with significant potential to reduce coal dependence, lower carbon emissions, and provide sustainable energy in the off-grid areas of South Africa’s Eastern Cape. However, due to wind variability, site-specific assessments are crucial for accurate resource estimation and investment risk mitigation. This study evaluates the wind energy potential at Fort Hare using six statistical distribution models: Weibull (WEI), Rayleigh (RAY), gamma (GAM), generalized extreme value (GEV), inverse Gaussian (IGA), and Gumbel (GUM). The analysis is based on three years (2021–2023) of hourly wind speed data at 10 m above ground level from the Fort Beaufort weather station. Parameters were estimated using the maximum likelihood method (MLM), and model performance was ranked using the total error (TE) metric. The results indicate an average wind speed of 2.60 m/s with a standard deviation of 1.85 m/s. The GEV distribution was the best fit (TE = 0.020), while the widely used Weibull distribution ranked third (TE = 0.5421), highlighting its limitations in capturing wind variability and extremes. This study underscores the importance of testing multiple models for accurate wind characterization and suggests improving the performance of the Weibull model through advanced parameter optimization, such as artificial intelligence. The wind power density was 31.52 W/m2, classifying the site as poor for large-scale electricity generation. The prevailing wind direction was southeast. Recommendations include deploying small-scale turbines and exploring augmentative systems to optimize wind energy utilization in the region.