## 3 Results
This discrepancy highlights the challenge of accurately predicting GHI under heavily clouded conditions, where the models struggled to adjust for the reduced solar input effectively. Figure 4: RMSE for 1-day-ahead forecasting of daily GHI was calculated using all models on the test dataset (2018) for the OMZ stations, categorized by the three sky conditions defined in the text. Figure 6 shows the GHI time series predicted one day in advance by the Prophet, ENAS, and Persistence (baseline) models. There was generally good agreement between the predicted and actual values. Although Prophet occasionally yielded less-accurate results, this could potentially be mitigated by retraining the models with longer time series data. Future work will incorporate larger data sets, with the goal of improving model accuracy. The persistence model demonstrates why, despite being an extremely simple model, it remains an efficient baseline for predicting GHI. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **All-Sky** & **All-Sky** & **Clear** & **Clear** & **Partly Cloudy** & **Partly Cloudy** & **Cloudy** & **Cloudy** \\ \hline
**Results** & **r** & **RMSE \%** & **r** & **RMSE \%** & **r** & **RMSE \%** & **r** & **RMSE \%** \\ \hline
**Persistence** & 0.71 & 22.55 & 0.82 & 14.39 & 0.52 & 27.79 & 0.39 & **65.22** \\
**RNN** & 0.71 & 24.36 & **0.89** & **10.83** & 0.62 & 29.81 & 0.66 & 102.12 \\
**Linear Regressor** & 0.70 & 21.26 & 0.89 & 17.95 & **0.64** & **19.23** & **0.71** & 89.49 \\
**Random Forest** & 0.70 & 20.75 & 0.87 & 15.21 & 0.60 & 20.78 & 0.57 & 85.26 \\
**XGBoost** & 0.71 & 20.64 & 0.87 & 15.01 & 0.62 & 20.35 & 0.63 & 87.69 \\
**SVM** & 0.71 & 20.43 & 0.89 & 13.95 & 0.60 & 21.85 & 0.62 & 81.81 \\
**KNN** & 0.57 & 24.10 & 0.81 & 15.96 & 0.53 & 24.27 & 0.46 & 109.28 \\
**Extra trees** & 0.71 & 20.51 & 0.89 & 14.95 & 0.63 & 20.23 & 0.62 & 86.85 \\
**Adaboost** & 0.70 & 20.94 & 0.88 & 13.73 & 0.58 & 22.11 & 0.46 & 89.45 \\
**ElasticNet** & 0.72 & 20.70 & 0.90 & 14.93 & 0.64 & 20.09 & 0.63 & 90.68 \\
**Ridge** & 0.71 & 20.43 & 0.88 & 14.27 & 0.59 & 21.53 & 0.63 & 81.85 \\
**Lasso** & 0.72 & 20.27 & 0.89 & 14.27 & 0.61 & 20.75 & 0.59 & 84.57 \\
**ENAS \({}^{*}\)** & **0.73 \({}^{*}\)** & **20.05 \({}^{*}\)** & 0.86 & 14.17 & 0.57 & 21.42 & 0.56 & 77.1 \\
**CNN** & 0.66 & 22.38 & 0.86 & 18.77 & 0.59 & 20.18 & 0.64 & 87.21 \\
**Meta’s Prophet** & 0.55 & 24.54 & 0.82 & 14.43 & 0.58 & 23.98 & 0.58 & 125.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance measures across all the models including RMSE % and r. Marked by an asterisk, ENAS demonstrates the best balance of these metrics across all sky conditions. The best results are highlighted with **bold text**. Figure 5: Comparison on the forecasted and measured next-day GHI for the test year 2018 using (**a**) the Prophet model and (**b**) ENAS according to three types of daily sky conditions: clear, partly cloudy, and cloudy. Statistical indicators of model performance are also provided. Despite the initial differences between each model, particularly during the optimization of their hyperparameters, the resulting performance metrics showed consistency. Figure 7 illustrates the behavior of r, indicating a stable trend hovering around 0.70 across the various models. This stability suggests that, despite model variations and tuning efforts, their ability to predict solar radiation remains relatively consistent within this range of correlation. Regarding the RMSE %, we observed a more unpredictable behavior, with values ranging between 20% and 25%. It is interesting to note that under all sky conditions, the ENAS model showed notable robustness, standing out for its more consistent performance and lower variability compared to the other atmospheric conditions evaluated.