## 5 Conclusions  
This study was based on the experimental data recorded and analyzed at 10 min intervals between September 2020 and June 2022 in Burgos (Spain). The GHUVE/GHI ratio was analyzed at different time intervals as a function of the sky type classified according to the ISO/CIE standard. The analysis of the GHUVE/GHI ratio yielded a gradual increase from dawn to noon (12:00 h) and a subsequent decrease until sunset. A greater dispersion of values was observed in the central hours of the day (10:00 a.m. to 2:00 p.m.). The relationship showed higher values with a higher dispersion for the months from May until August and lower values in December and January. The value of the ratio and the data dispersion into the three categories (overcast, intermediate, and clear skies) was very similar. Different models were analyzed to determine GHUVE based on meteorological and radiative variables collected in Burgos, using multilinear regression models (MLR) and artificial neural network (ANN) for all skies, overcast, intermediate, and clear skies. The use of variables from databases or satellite observation was not considered to guarantee, as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Number of Input Variables**} \\ \hline  & & **3** & **4** & **5** & **6** \\ \multirow{4}{*}{ANN} & 1 & 0 & 0 & 0 & 0 \\  & 2 & 0 & 2 & 5 & 22 \\  & 3 & 8 & 14 & 25 & 27 \\  & 4 & & 18 & 28 & 41 \\  & 5 & & & 30 & 48 \\  & 6 & & & 57 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Percentage of generated GHUVE ANN models, according to number of input variables and number of neurons, with nRSME \(<\) 15%.. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of** & **Meteorological** & **RSME(\%)** \\ \multirow{2}{*}{**ANN**} & **Variables/Neurons** & **Variables** & **RSME(\%)** \\  & **in the Hidden Layer** & **Variables** & **RSME(\%)** \\ \hline Model 1 & 5/2 & \(\mathrm{GHI},\theta_{\mathrm{z}},\varepsilon,\mathrm{T},\mathrm{k}_{\mathrm{t}}\) & 14.49 \\ Model 2 & 5/3 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{T},\mathrm{k}_{\mathrm{t}},\psi\) & 14.16 \\ Model 3 & 5/4 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{T},\mathrm{CC},\psi\) & 13.34 \\ Model 4 & 5/5 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{T},\mathrm{\Delta},\psi\) & 13.01 \\ Model 5 & 6/2 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{k}_{\mathrm{t}},\varepsilon,\mathrm{T},\mathrm{RH}\) & 14.11 \\ Model 6 & 6/3 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{DHI},\mathrm{T},\mathrm{RH},\psi\) & 13.59 \\ Model 7 & 6/4 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{T},\mathrm{RH},\mathrm{CC},\psi\) & 12.94 \\ Model 8 & 6/5 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{T},\mathrm{RH},\mathrm{CC},\psi\) & 12.53 \\ Model 9 & 6/6 & \(\mathrm{GHI},\theta_{\mathrm{z}},\mathrm{k},\mathrm{T},\mathrm{CC},\psi\) & 12.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance (nRSME (%)) of ANN models of GHUVE, generated from combinations of five and six experimental meteorological variables and from two to six neurons in the hidden layer. far as possible, the applicability of locally obtained models in other locations using data from ground meteorological facilities. The MLR model fitting results presented high determination coefficients with the experimental data (R\({}^{2}>0.92\)) for all skies, overcast, intermediate, and clear skies. However, the nRMSE value was elevated, exceeding 20% in the four cases. Since the MLR models did not perform as expected, GHUVE was modeled by ANN models, considering the variables used for MLR modeling for each sky type. In all four cases, a good determination coefficient was obtained, R\({}^{2}>0.95\). The nRSME obtained varied between 12 and 21%. The best results were obtained for clear skies (R\({}^{2}>0.97\) and nRMSE \(<12.4\%\)). For all skies, the optimal number of variables was evaluated to achieve an nRMSE of less than 15% by means of ANN models.