## 4 Results  
### Wind Energy  
#### 4.1.1 Wind Resource Analysis  
We analyzed measured wind data collected at the Ghoubbet site. Temporal wind speed data were recorded for two years with 10-min intervals and preprocessed by converting them into hourly and monthly mean wind speeds at 80 m. A statistical analysis of the  
Figure 4: Schematic of the dry steam cycle with electrolyzer for Asal-Ghoubbet hydrogen production.  
Figure 5: Schematic of the single flash cycle with electrolyzer for Asal-Ghoubbet hydrogen production.  
wind data was first performed, which predicted the energy output of a typical wind energy conversion. The wind potential characterization is based on the Weibull statistical distribution. Average wind speeds and wind characteristics are inferred from the temporal distribution of the study site. Four Weibull methods (i.e., moment method (MM), empirical methods of [PERSON] (EMJ), WAsP, and maximum likelihood method (MLM)) were used to predict the existing wind potential at the height of 80 m. The Weibull distribution and cumulative density function at the AG Rift are shown in Figure 6a,b.  
Analysis of the distribution diagram reveals that the moment method (MM), empirical method of [PERSON] (EMJ), and maximum likelihood method (MLM) have the best accuracy in terms of the Weibull distribution compared to the WAsP Method (WM), which underestimates the wind distribution (Figure 6a). This difference is also noticed in the cumulative density function (Figure 6b), where the three methods are homogeneous compared to the WM values. Statistical tests of R\({}^{2}\), RMSE, and MAE were also performed to strengthen  
Figure 6: (**a**) Annual 80 m wind speed Weibull distribution (PDF) and (**b**) cumulative distribution function (CDF) at level of 80 m for Ghoubbet.  
this evaluation in respect of the quantitative approach. In our case, the values of the Weibull shape parameter (k) range from 2.65 to 2.96 at Asal-Ghoubbet at the height 80 m (Table 1). In our case, the Weibull shape parameter (k) values range from 2.65 to 2.96 at Asal-Ghoubbet at the height of 80 m (Table 1). The Weibull scale parameter (c) varies between 11.19 m/s and 11.25 m/s (Table 1). Indeed, the most accurate method is used to estimate the capacity factor of the Ghoubbet wind speed regime. The performance of the Weibull methods in Table 1 reveals that the moment method has the highest value of coefficient of determination (R\({}^{2}\)) and the lowest value of mean square error (RMSE) and mean absolute error (MAE). On this basis, the moment method is the most appropriate for estimating the Weibull parameter in Asal-Ghoubbet. The EMJ method presents satisfactory results to fit the wind speed observations at a level of 80 m at Asal-Ghoubbet (Table 1). On the other hand, the average wind speeds predicted by the four methods are about 10 m/s, indicating that the match to the measured data is very high (Supplementary Table S2).  
The wind speed data at the height of 80 m were extrapolated from the wind data measured at the height of 60 m using the lowest power coefficient. The average annual power density is estimated to be about 904.45 W/m\({}^{2}\) for an altitude of 80 m at the Ghoubbet location.  
According to the power density classification established as [11]:  
* Fair (PD < 100 W/m\({}^{2}\))
* Fairly good (100 W/m\({}^{2}\)\(\leq\) PD < 300 W/m\({}^{2}\))
* Good (300 W/m\({}^{2}\)\(\leq\) PD < 700 W/m\({}^{2}\))
* Very good (PD \(\geq\) 700 W/m\({}^{2}\))  
Wind direction analysis is essential for the planning of wind turbine installations. The frequencies of wind directions at 60 m during 2015 are presented in Supplementary Figure S2. The distribution of polar diagrams indicates that the East (90\({}^{\circ}\)) and South East (135\({}^{\circ}\)) sectors are the most reactive and have the highest wind frequencies. The average annual wind speed at Ghoubbet is estimated at 10 m/s (Supplementary Table S4).