Abstract
Accurate statistical modeling of wind speed variability is crucial for assessing wind energy potential, particularly in regions with low wind speeds and significant calm hours. This study evaluates the Champernowne distribution as a novel model for wind speed analysis, comparing its performance with the two-parameter Weibull, three-parameter Weibull, and Rayleigh-Rice distributions. Wind speed data at 10 m hub height over three years (2021-2023) from Ben Guerir, Morocco, were analyzed using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Coefficient of Determination (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The Champernowne distribution outperformed the other models across all metrics, achieving the lowest RMSE (0.00036), MAE (0.00022), AIC (650.52), and BIC (689.46), and the highest R(2) (0.99998). Its ability to capture calm hours and extreme wind speeds provided more accurate power density estimates, with errors averaging 23%, compared to 33% and 42% for the Weibull and Rayleigh-Rice distributions, respectively. Despite low mean wind speeds (2.7 m/s), Ben Guerir's ground-based power density ranged from 18 to 54 W/m(2). The results suggest that conventional large-scale wind energy projects are unsuitable for Ben Guerir. Instead, small Vertical-Axis Wind Turbines (VAWTs) or alternative strategies should be considered. The Champernowne distribution's robustness makes it a valuable tool for wind energy assessments, especially in regions with intermittent wind patterns, providing a foundation for more accurate modeling and energy planning.