When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the testing phase of Coefficient of Determination (R = 0.9005), Mean Absolute Percentage Error (MAPE = 8.40%), and Relative Root Mean Square Error (rRMSE = 12.56%). Nevertheless, the ANFIS model outperformed the GA-ANN model in forecasting daily GSR, with the best values of indicators when testing the model being R = 0.9374, MAPE = 7.78%, and rRMSE = 10.54%. Generally, we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved, and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory. The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

This study was conducted for six cities in southern Algeria, where the accuracy of three models—support vector machines (SVM), artificial neural networks (ANN) and a novel hybrid firefly algorithm-based model (FFA-ANN)—were investigated when estimating global solar irradiation throughout an eleven-year period, utilizing nine input parameters as input data. The goal of our novel suggested a hybrid FFA-ANN model, where we relied on the optimization Firefly algorithm to enhance the ANN model created. Despite the fact that the ANN and SVM models produced promising results, our suggested FFA-ANN hybrid model outperformed the stand-alone ANN-based model using three statistical factors—correlation coefficient, relative root mean squared error and mean absolute percent error—with the best values of (R = 0.9321, rRMSE = 9.35% and MAPE = 6.29%). The findings demonstrated that FFA-ANN was preferable to the optimized SVM and ANN models when forecasting daily global solar irradiation in all zones. Furthermore, after comparing the combinations, the study's findings showed that the ANN model depended on: Extraterrestrial solar irradiation (H₀), declination and average temperature (T_avg) together with relative humidity (RH) as inputs in order to estimate daily sun radiation. Thus, the findings of this study suggest that in regions with dry climates and other places with comparable climates, the created model may be used to estimate daily global solar radiation whenever data is accessible.