## 5 Results and Discussion
### Scenario 3  
This section explores the results related to Scenario 3, which are shown in Table 3. The table shows the FS, MAE, and RMSE metrics for the two baseline models and the three proposed models.  
With these results, we can analyze how adding extra weather details affects the performance of the ST_CNN_v1,ST_CNN_v2, and ST_Dilated_CNN models in predicting GHI at different weather stations.  
It can also be observed that the LR and RF models have similar results in model forecast skill for stations ZA02 and P07. However, for stations LE05 and B04, RF shows higher values, while LR improves on RF for stations P03, SG01, and SO01. These results show that these models are more closely tied to the variability present at each of the stations evaluated. Nevertheless, it can be concluded that both models have similar predictive capacity for this study.  
The ST_CNN_v1 model improves the FS of the LR model at stations LE05, P07, and SG01, and the RF model at stations P03, P07, and SG01. The ST_CNN_v2 and ST_Dilated_CNN models improve on the FS of all baseline models for all seasons. The ST_Dilated_CNN model improves on the ST_CNN_v1 and ST_CNN_v2 models for almost all stations, except for P03 and SO01, on which the ST_CNN_v2 model outperforms the others with an FS of 27.76 and 27.72, respectively.  
It should be noted that CNN-based models generally perform better than LR and RF models, although there may be changes depending on the specific station. In particular, the ST_CNN_v2 and ST_Dilated_CNN models show their ability to learn the variability present by improving the FS for all stations.  
Figure 9 shows the FS of the models for all the stations studied, and evidences the ability of the ST_CNN_v1, ST_CNN_v2, and ST_Dilated_CNN models to improve the quality of the forecast for all stations, with a significant difference with respect to the baseline models.  
In Figure 9, it can be observed that at stations such as ZA02 and LE05, the FS values are generally higher, which could indicate that their weather conditions are more predictable and therefore the models are able to better fit the data measured at the site. At the same time, there are stations such as BU04, P03, and SO01 that generally show lower FS values. These changes may indicate that these sites are subject to greater climatic variability and therefore it may be necessary to better adapt the model parameters to achieve a better result.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Linear Regression**} & \multicolumn{4}{c}{**Random Forest**} & \multicolumn{4}{c}{**ST\_CNN\_v1**} & \multicolumn{4}{c}{**ST\_CNN\_v2**} & \multicolumn{4}{c}{**ST\_Dilated\_CNN**} \\ \hline \multirow{3}{*}{**StationMetric**} & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** & **FS** & **MAE** & **RMSE** \\  & **(\%)** & **(W/m\({}^{2}\))** & **(W/m\({}^{2}\))** & **(\%)** & **(W/m\({}^{2}\))** & **(\%)** & **(\%)** & **(W/m\({}^{2}\))** & **(\%)** & **(\%)** & **(W/m\({}^{2}\))** & **(\%)** & **(\%)** & **(W/m\({}^{2}\))** & **(\%)** & **(\%)** & **(W/m\({}^{2}\))** \\ \hline BU04 & 25.59 & 43.76 The LR, RF, and ST_CNN_v1 models show variable behavior dependent on particular stations. The LR model for stations LE05 and BU04 improves on the RF and ST_CNN_v1 models, while RF improves on the LR and ST_CNN_v1 models for stations P03 and SO01. The ST_CNN_v1 model improves on the baselines for stations PO07 and SG01.  
In this scenario, the ST_Dilated_CNN model improves the FS of the ST_CNN_v2 model for stations BU04, LE05, P07, and SG01, while the ST_CNN_v2 model improves it for the rest of the stations, although the margin of improvement is below one percentage point. Both models improve the FS compared to all other models.