NIYONGABO Prime ¹ and NIBITEGEKA Onésime ¹

¹BURUNDI MILITARY ACADEMY (INSTITUT SUPERIEUR DES CADRES MILITAIRES: ISCAM), AVENUE DES FORCES ARMEES, BP 2705, BUJUMBURA-BURUNDI.

Today, there is a significant lack of information in the open and grey literature pertaining to energy scenarios and development in addressing the renewable energy technologies in Burundi. In order to assess the potential and suitability of a location to deploy a wind farm, it is essential to have a model of Weibull distribution from the parametric estimation. The measurement procedure was carried out at the edge of the Lake Tanganyika in Burundi. In the work, the frequency distribution of wind speed has shown dissimilar wind power densities for some wind speed. The probability distribution function of wind speed defined the wind power density of our location of study. Weibull distribution function is well known and commonly used frequency distribution in wind energy. It is a two parameter function known as shape (K) and scale (λ) parameters. The scale parameter K describes the abscissa scale of a data distribution plot, whereas the Weibull shape λ parameter characterizes the width of the data distribution. There are various methods available to compute the parameters of Weibull distribution. In this work, we have used nine different numerical methods to examine the calculation of the parameters of Weibull distribution at the heights 60m, 80m and 100m to estimate the wind power density. The time series wind data were recorded using a SODAR instrument. The aim of this study is to identify the more accurate method for computing wind power density of our selected region using Weibull distribution estimate methods. The SODAR measurements have shown successful results and may be used as an alternative meteorological data collection comparing the results with cup anemometer. The accuracy of our methods of study is judged based on goodness of fit test: Coefficient Determination (R2), Chi-Square (Χ2), Root Mean Square Error Test (RMSE) and Mean Absolute Percentage Error (MAPE). The paper is useful to policy makers, local and international investors, scientists and engineers in the energy sector with the wind energy option.