## 5 Discussion  
### Wind Energy  
The analysis of the distribution diagram reveals that the moment method (MM), empirical method of [PERSON] (EMI), 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 terms of the cumulative density function (Figure 6b), where the three methods are homogeneous compared to the WM values. The performance of the Weibull methods in Table 1 reveals that the moment method has the highest value of the 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 S4). In addition, the wind potential of Ghoubbet reveals a promising site for the installation of a large-scale wind farm for electricity and hydrogen production. Moreover, the power densities predicted by the four Weibull fitting methods are estimated to be 921.3745 W/m\({}^{2}\), 912.1199 W/m\({}^{2}\), 919.1252 W/m\({}^{2}\), and 878.3873 W/m\({}^{2}\), respectively, for the MM, the MLM, the EMJ, and the WM. Nevertheless, the average monthly wind power density depends on the season. It can be observed that for the season June-September, the power density is at its lowest (192 to 194 W/m\({}^{2}\)) for the year 2015 (Figure 7a) and can reach a wind speed below 5 m/s in several hours (Figure 7b). The performances of 42 wind turbines were analyzed in terms of energy production and capacity factors to allow for the selection of the turbine that best matches the wind regime of the given site. The calculated C\({}_{\text{f}}\) of the 42 turbines ranges from 0.114 to 0.849 (Supplementary Table S5). The Yinhe GX113-2.5 MW turbine with a height of 80 m was found to be the best turbine model with a C\({}_{\text{f}}\) of 84.9% for Asal-Ghoubbet (Supplementary Table S5). The annual energy produced by the \"Yinhe GX113-2.5 MW\" turbine is about 18,606.64 MWh/year. According to the result, the capacity factor is high when the values of cut-in speed, rated speed, and cut-out speed are low. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Thermodynamic Operation** & \begin{tabular}{c} **Electrolyser Coupled** \\ **with Geothermal** \\ **Power Plant** \\ \end{tabular} & \begin{tabular}{c} **Maximum Work** \\ **Requirement** \\ **(kJ/kg)** \\ \end{tabular} & \begin{tabular}{c} **Minimum Work Input** \\ **(kWh/kg)** \\ \end{tabular} & \
\begin{tabular}{c} **H\({}_{\text{2}}\) **Produced** \\ **(fons/Year)** \\ \end{tabular} &
\begin{tabular}{c} **LCOH** \\ **(5/kg)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Reversible case} & Single flash-PEME\({}_{\text{m}}\) & 362.05 & 117,651 & 32.68 & 731.44 & 5.51 \\  & Dry steam-PEME\({}_{\text{m}}\) & 362.05 & 117,651 & 32.68 & 3308.83 & 3.97 \\  & Single flash-HTE & 362.05 & 91,858 & 25.52 & 1426.66 & 4.56 \\  & Dry steam-HTE & 362.05 & 91,858 & 25.52 & 6453.78 & 3.31 \\ \hline \multirow{4}{*}{Irreversible case} & Single flash-PEME\({}_{\text{m}}\) & 362.05 & 190,602 & 52.95 & 451.51 & 8.16 \\  & Dry steam-PEME\({}_{\text{m}}\) & 362.05 & 190,602 & 52.95 & 2042.49 & 5.80 \\ \cline{1-1}  & Single flash-HTE & 362.05 & 97,721 & 27.14 & 1341.06 & 4.78 \\ \cline{1-1}  & Dry steam-HTE & 362.05 & 97,721 & 27.14 & 6066.56 & 3.46 \\ \hline \end{tabular}
\end{table} 
Table 5: Result of green hydrogen production supply with the geothermal energy analysis. Therefore, the selected wind turbine was used to evaluate the economic feasibility of electricity generation and hydrogen production in the Asal-Ghoubbet area.