Batouri is the town head quarter of the Kadey division in the East Region of Cameroon. It is the second largest municipality in the province after the regional capital Bertoua. It is located on the main (though unpaved) road connecting Bertoua to the Central African Republic. Batouri is at Latitude N at 4˚26' and Longitude E at 14˚43'. Figure 1 presents Cameroon’s map with the location of Batouri [2] .

Data used for this study has been collected and analyzed by using lots of materials belonging to the Center of Research on Renewable Energy (CRER) of Institute for Geological and Mining Research (IRGM). A weather vane and anemometer installed at a height of 10 meters above the ground, a sensor station and data recovery console, a computer and software Weather link are materials used

for this study. Matlab software has been used for numerical equation resolution. Wind speed data used was collected during two years and two months with a step of 30 minutes (period from April 2014 to May 2016). Some wind speed data are available in time series format, in which each data point represents either an average wind speed over a time period of 30 min. The frequency distribution of wind speed measured has been classified for an amplitude of 0.2 (the first one being 0 - 1.2 to avoid the very small speed) of wind speed from data collected on the study period.

The Weibull distribution is a continuous probability distribution that can fit an extensive range of distribution shapes. Like the normal distribution, the Weibull distribution describes the probabilities associated with continuous data. However, unlike the normal distribution, it can also model skewed data. In fact, its extreme flexibility allows it to model both left- and right-skewed data. The Weibull distribution provides a close approximation to the probability laws of many natural phenomena. It has been used to represent wind speed distribution for application in wind load studies for some time. The Weibull distribution function, has two parameters function K and C. The formula for the probability density function of the general Weibull distribution (the probability density function of wind speed v) is:

f ( v ) = K C × ( v C ) K − 1 × exp ( − ( v C ) K ) (1)

where: v is the wind speed (m/s).

C is the Weibull scale parameter (m/s).

K is the Weibull shape parameter.

The cumulative distribution function of the velocity v is the integral of the probability density function. Thus,

F ( v ) = ∫ 0 ∞ f ( v ) d v = 1 − exp ( − ( v C ) K ) (2)

The average wind velocity of a regime, following the Weibull distribution is given by:

v m = ∫ 0 ∞ f ( v ) × v d v (3)

Upon substituting Equation (1) and considering the standard gamma function

( Γ n = ∫ 0 ∞ exp ( − x ) ⋅ x 1 K d x ), we deduce:

V m = C × Γ ( 1 + 1 K ) (4)

The distribution of Weibull is suitable for the description of statistics properties of wind. There are common methods for determining parameters K and C as: Graphical method, Empirical method, Energy pattern factor method, Moment method, Equivalent energy Method, [1] [3] [11] [12] .

Five methods used to estimate the parameters of the Weibull wind speed distribution are presented below.

There are lots of tests which can be used to analyze the accuracy of the methods estimation of parameters. In this study, root mean square error (RMSE) and the correlation coefficient R² have been used from the following equations:

R M S E = ( 1 N ∑ i = 1 N ( y i − x i ) 2 ) 1 2 (16)

R 2 = ∑ i = 1 N ( y i − z ) 2 − ∑ i = 1 N ( y i − x i ) 2 ∑ i = 1 N ( y i − z i ) 2 (17)

where: N is the number of observations, y_i is the actual data, x_i is the predicted data of Weibull distribution, z_i is the mean wind speed [1] [19] .

The main components that determine the wind energy potential of a site are the energy density and the energy available in the wind regime over some period of time. The available power in the wind flowing at mean speed v through a wind rotor blade with sweep area A at any given site can be estimated as [19] [20] [21] :

P ( v ) = 1 2 × ρ × A × v 3 (18)

where: ρ (kg/m³) is the volumic mass of air.

Then, the power in the wind per unit of area is:

P D ( v ) = 1 2 × ρ × v 3 (19)

Using expression (19) with the Weibull probability distribution the wind energy density of a site expressed as,

E D = ∫ 0 ∞ P D ( v ) × f ( v ) × d v (20)

Using Equations (1), (4) and (19), Equation (20) simplifies to,

E D = ρ × C 3 2 × 3 K × Γ ( 3 K ) (21)

Table 1 presents the mean wind speed from data.

The Weilbull distribution of the years 2014 2015 and 2016 are respectively represented by Figures 2-4, described by its probability function f(v), versus the mean wind speed.

Tables 2-4 show the statistical analysis results.

Table 5 presents a global rank of numerical methods performance. It is important to note that only one column was enough to rank the method, since the three criteria (RMSE, X² and R²) gave the same results.

Firstly, the statistical analysis presented above shows that the values of the performance tools used namely RMSE, X², and R² are very close to each other whatever the digital method used. In addition, viewing Figures 2-4 it appears that two of the methods (Energy Pattern Factor and Equivalent Energy) give curves having a better confusion with the frequency histogram of wind speed measured, consequently giving a better approximation of the parameters K and C. Moreover, from the classification table of performance tools it appears that the Energy Pattern Factor methods has the best performance with the rank 3rd, 1st and 1st 3rd respectively for R², RMSE and X².

Secondly, for each numerical method, according to the modeling of wind speed by weibull distribution, the values of parameter K has some variation. Its value is included between 3.5499 and 6.1696. The values of C are included between 2.0642 to 2.4659.

Finally, since the energy pattern factor method has the best performance mainly for the year, then the best approximation of these parameters can be: K = 3.8262 and C = 2.4659.

The average wind speed is around 2.3 m/s. Since for a suitable installation of conventional horizontal axis wind turbines whose cut-in velocity is about 4 m/s [1] [21] , Batouri is not suitable for wind energy. But this speed cannot be negligible for mainly domestical need as lighting, battery charging, radio, small water pumping etc. Especially in dry season in a locality with the rate of access to electricity estimated at 17.35%.