## 3 Results  
In this section, we present the results of our comprehensive study on the prediction of daily GHI patterns in Zacatecas using a diverse array of regression and neural network models. The findings from our study highlight the importance of selecting appropriate forecasting models for predicting daily solar radiation patterns in Zacatecas. The use of both traditional and advanced techniques allowed for a comprehensive evaluation of their effectiveness in capturing the complexities of GHI data. The correlation coefficient and RMSE % metrics provided a robust framework for comparing model performance, emphasizing the need for accurate and reliable prediction methods. This comparative analysis underscores that while traditional regression models offer a baseline, advanced techniques, including regression methods and neural networks, are crucial for addressing the inherent challenges of forecasting in variable environmental conditions. The diverse array of models tested in this study demonstrates that no single approach universally excels, but rather, the choice of model should be guided by its ability to handle specific data characteristics and forecasting requirements. These insights contribute to a deeper understanding of model performance in GHI prediction and offer valuable guidance for future research and practical applications in similar regions. Each model and technique, including traditional methods such as linear regression, random forest, XGBoost, SVM, KNN, extra trees, Adaboost, ElasticNet, ridge, lasso, and advanced approaches like ENAS, CNN, RNN, and Meta's Prophet, was meticulously trained and optimized to find the best configuration of their respective hyperparameters. This rigorous approach aimed to uncover the most effective models for accurately forecasting GHI levels under the different environmental conditions observed in Zacatecas from 2015 to 2018. We utilized three years for training and validation across each of the models employed in this study. This method ensured a fair and robust comparison of the models in forecasting daily solar radiation levels in Zacatecas. When evaluating the forecasting models, it became apparent that their performance varied significantly across different weather conditions. The models were tested across a range of scenarios, including clear, partly cloudy, and cloudy conditions. The results indicate that while the models generally performed well under clear and partly cloudy conditions, their accuracy diminished under cloudy conditions. This disparity is attributed to the limited number of cloudy days in the dataset, which restricts the models' ability to generalize effectively to such scenarios. This variability in performance highlights the need for further refinement and potential augmentation of the models to better handle less-frequent weather conditions. The RMSE % results are displayed in Figure 4, showcasing the performance of each model under the defined conditions. Due to the limited number of cloudy days, the models struggled to generalize accurately; however, they demonstrated excellent performances under other weather conditions. Table 2 presents the evaluation metrics and the actual performance of each model. While overall, the models performed well, ENAS demonstrated the best performance across the varied conditions. RNN excelled in predicting clear days, while linear regression stood out for partially cloudy days. The persistence model showed the lowest error percentage on cloudy days, primarily due to the limited occurrence of cloudy days (\(<\)9%), which challenges more sophisticated methods when learning their characteristics. Figure 5 illustrates scatter plots comparing forecasted and measured outcomes for ENAS (best) and Prophet (worst), segmented according to three daily cloudiness categories. The ENAS predictions show a notable alignment with measured values, particularly on days categorized as partially cloudy. In contrast, both the ENAS and Prophet models demonstrated substantial overestimation for cloudy days.