## 5 Results and Discussion
### Comparison between Scenarios  
Comparing the results of the three scenarios (each applying a different approach to data input) provides a better understanding of the impact of including variables in the solar forecast. The three scenarios involve the use of different input features: the first scenario exploits only the global horizontal irradiance of the target station and its close neighbors with 48 lags; the second scenario introduces astronomical variables in addition to the initial features; and the third scenario additionally incorporates astronomical and meteorological variables. In Scenario 1, the modelling of global horizontal irradiance only is simple and computationally more efficient, since it focuses only on historical irradiance data, but there is a limitation of potentially missing certain spatio-temporal nuances and astronomical patterns that contribute to solar irradiance dynamics. In Scenario 2, when astronomical variables are added, the predictive capability of the models is improved, as demonstrated by higher FS values and lower MAE and RMSE values at several stations. Stations such as SO01, P07, and SG01 benefit notably from the addition of astronomical variables, showing improved accuracy and better model performance. In Scenario 3, when the modelling combines astronomical and meteorological variables, further improvements in the performance of the models can be found, resulting in reduced MAE and RMSE values, particularly at stations P07 and SO01. Upon closer examination of the results in the results tables for the three scenarios, it can be observed that the incorporation of astronomical variables produces noticeable impacts on the prediction of solar radiation at different meteorological stations. In particular, the ST_Dilated_CNN model shows better performance in FS, MAE, and RMSE in a systematic way when compared to the ST_CNN_v1 and ST_CNN_v2 models, with different degrees of improvement observed at different stations. Figure 9: Comparative FS performance of three distinct models—ST_CNN_v1, ST_CNN_v2, and ST_Dilated_CNN—and baseline models across various meteorological stations in Scenario 3. The station that shows a distinct behavior is station SO01, which does not clearly benefit from the inclusion of the astronomical and meteorological variables. The ST_Dilated_CNN model obtains the best FS results (28.32%) for Scenario 1 and reduced MAE and RMSE values, which underlines its ability to capture the solar radiation dynamics at this location. The P07 and SG01 stations also experience notable benefits from the integration of astronomical variables, as evidenced by consistently elevated FS values and decreased MAE and RMSE metrics. The ST_Dilated_CNN's performance surpasses its counterparts, accentuating its proficiency in leveraging astronomical features for improved predictions. Stations with marginal benefit are BU04, LE05, P03, and ZA02; while still benefiting from the inclusion of astronomical variables, these stations exhibit more modest improvements. ST_Dilated_CNN consistently outperforms ST_CNN_v1 and ST_CNN_v2, but the differences in FS, MAE, and RMSE are less pronounced compared to the aforementioned stations. When analyzing the ST_Dilated_CNN model for the three scenarios, it is found that the FS values of Scenarios 2 and 3 improve with respect to Scenario 1, with differences of one to two percentage points (Appendices A and B can be consulted for a visual representation of the delta FS values). The ST_CNN_v2 model is the only one that benefits in all stations for all scenarios. The ST_Dilated_CNN model, although it also benefits for stations SO01 and ZA02, shows different behavior in Scenario 2, and for station SO01 in Scenario 3. It is estimated that this different behavior is due to the fact that these two stations have the most spatially distant neighbors of all the stations studied, as can be seen in Figure 1. Therefore, the input of data from neighbors can create noise, reducing the network's ability to extract valid information.