## 5 Results and Discussion
### Scenario 1  
The results of the three proposed models (ST_CNN_v1,ST_CNN_v2, and ST_Dilated_CNN) for the various meteorological stations in Scenario 1 and comparisons with the baseline models are presented in Table 1 (by means of the FS, MAE, and RMSE metrics achieved by the trained models). When analyzed in detail, the metrics reveal that the proposed ST_Dilated_CNN model obtains higher values for the FS percentages systematically, indicating its ability to better capture the variance within the dataset. The high performance of this model is especially noticeable at stations ZA02 and LE05, where it outperforms other models by a difference of one to two percentage points. The ST_CNN_v2 model has the lowest mean absolute error (MAE) values for most stations, suggesting that its predictions are closer to the observed data. As an illustration of its high performance, the MAE of ST_CNN_v2 for station LE05 is significantly lower than that of the linear regression model. The ST_CNN_v2 model outperforms the ST_Dilated_CNN model in terms of accuracy in forecasting both mean and extreme values, despite the latter's general superiority in capturing the variability of the data. For instance, ST_Dilated_CNN outperforms both ST_CNN_v1 and ST_CNN_v2 at station BU04 with an FS of 28.71%, an MAE of 40.54 W/m\({}^{2}\), and an RMSE of 66.39 W/m\({}^{2}\). Similar trends are shown at the LE05, P03, P07, SG01, SO01, and ZA02 stations, where the prediction accuracy of ST_Dilated_CNN is consistently high. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{2}{c}{**Linear Regression**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**ST\_CNN\_v1**} & \multicolumn{3}{c}{**ST\_CNN\_v2**} & \multicolumn{3}{c}{**ST\_Dilated_CNN**} \\ \cline{2-13}  & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** \\ (**s**) & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** & **(F/s)** & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** & **(F/s)** & **(WIn the context of the table provided, the linear regression and Random Forest models serve as benchmark comparisons to evaluate the performance of more complex models, such as those proposed in this work (ST_CNN_v1, ST_CNN_v2, and ST_Dilated_CNN). The linear regression model, often used as a starting point in predictive analysis, shows moderate performance in all seasons. However, the ST_CNN_v2 model consistently outperforms it. In general, the Random Forest model achieves higher FS percentages and lower RMSE values, suggesting greater efficiency in handling variance and outliers. The ST_CNN_v1 model has mixed results; in stations like P03, BU04, ZA02, it outperforms the baseline models, but it obtains the worst results for station SO01. Despite this, the ST_CNN_v2 and ST_Dilated_CNN models demonstrate higher FS. When analyzed especially in terms of absolute error, this highlights the advantages of employing advanced deep learning techniques for this type of solar data analysis. Figure 7 presents a plot of the FS values achieved by the trained models for each of the stations studied. The models are shown in the following order: LR, RF, ST_CNN_v1, ST_CNN_v2, ST_Dilated_CNN. It is shown that, for station ZA02, the ST_CNN_v1 model improves on the baseline models, although it has a discrete result compared to those obtained by the other two deep learning models. This model presents less homogeneous results since, for stations SO01, P03, and BU04, it improves on the baselines, while for stations P05 and SO01, it delivers worse results. The ST_Dilated_CNN and ST_CNN_v2 models present the highest FS value for stations ZA02 and LE05. Although the ST_CNN_v2 model can equal this at station ZA02, the same does not occur for the rest of the stations, where the ST_Dilated_CNN model outperforms the rest of the models.