Statistical Analysis of Wind Speed Characteristics Using the Weibull Distribution at Selected African Stations

The Weibull distribution (WD) is widely recognized as an effective statistical tool for characterizing wind speed (WS) variability.This study investigates the applicability of the WD to analyze WS data from a selection of African stations, with data spanning from 2000 to 2023, obtained from the Power Data archive in comma-separated values (CSV) format.The analysis aimed to assess the distribution's ability to represent the variations in WS across different regions in Africa.The results reveal significant spatial variability in the Weibull parameters across the selected stations.wind direction patterns were analyzed, with the highest frequency recorded from the eastnorth-east (ENE) direction, reaching a value of approximately 400 at certain locations.The lowest wind direction frequencies were observed in Abuja, where the predominant directions were north-northwest (NNW) and north (N).The probability distribution of WS demonstrated a considerable range, with Abuja exhibiting the highest values (exceeding 0.5), while Tunis recorded the lowest values (approximately 0.2).The mean WS for each location varied over the year, with Nairobi experiencing the highest recorded mean WS in October (5.72 m/s), accompanied by a standard deviation of 1.22 m/s.In contrast, the lowest mean WS was observed in Luanda during September (1.72 m/s), with a standard deviation of 0.46 m/s.The maximum and minimum wind power density (PDw) recorded across the selected station are (> 100 W/m 2 ) and (> 18 W/m 2 ).These findings highlight the considerable potential for wind energy across Africa, emphasizing the importance of incorporating wind energy into the region's renewable energy strategy.The results underscore the need for region-specific energy policies and further research to optimize the utilization of wind resources for sustainable development in Africa.