## 3 Results and Discussion
### Performance of Hybrid Neural Network Models and Its Comparison for Different Geopolitical Zones  
CNN-ANN had the best prediction performance for the two west African countries. While the r, MAE, RMSE, and MAPE values for the model application are 0.9682, 37.74 W/m2, 87.54 W/m2, and 14.39% for Ghana; it is 0.9762, 30.99 W/m2, 78.80 W/m2, and 11.31% for Nigeria, respectively. The integration of CNN refines the performance of the ANN model in this region by reducing the errors (MAE, MAPE), and the difference  
Figure 10: CNN-ANN predictive performance in comparison to other models for Southern Africa.  
Figure 9: CNN-ANN-LSTM predictive performance in comparison to other models for Algeria in North Africa.  
in models' training and test (ARMSE, \(\Delta\)MAE, \(\lambda_{\text{MAE}}\), \(\lambda_{\text{RMSE}}\)) performance. The predictive performance comparison of the three models for hourly solar radiation prediction over 24 h for one west African country is illustrated in Figure 6. The performance of the model also shows that the CNN-ANN can be used for hourly solar radiation prediction for any country in the region (West African).  
In East Africa, CNN-LSTM-ANN had the best performance and is most accurate for hourly solar radiation in Ethiopia and Somalia. As seen in Table 4, the performance of the CNN-LSTM-ANN hybrid model is better and more refined than that of the ANN model and CNN-ANN model. It also had the highest r-value (0.9800 for Ethiopia; 0.9904 for Somalia) and the least MAE, RMSE, and MAPE (25.89 W/m\({}^{2}\), 72.72 W/m\({}^{2}\) W/m\({}^{2}\), and 9.31% for Ethiopia; 16.60 W/m\({}^{2}\), 51.54 W/m\({}^{2}\), and 5.725% for Somalia). The integration of one layer of LSTM with the CNN and ANN models smoothens the predictive performance of the model as seen in Figure 7 (where the predictive performance of the three models is compared based on their hourly solar radiation prediction with 24-h).  
As discussed in the preceding section, CNN-ANN has the most accurate predictive performance for Chad. This model also has the best for the Central African Republic (Table 4). The hourly solar irradiation prediction illustrated in Figure 8 further shows that the CNN-ANN is the closest to the true value. It is however noteworthy that CNN-LSTM-ANN had the least performance out of the three models considered for this region.  
Contrary to the model performance in other regions (where one model is good for both countries), the two-hybrid model had the most accurate predictive performance in North Africa. While CNN-ANN is the best model for Egypt, CNN-LSTM-ANN had the best performance for Algeria. The r-value, MAE, RMSE, and MAPE for the two countries respectively are 0.9925, 17. 25 W/m\({}^{2}\), 48.33 W/m\({}^{2}\), and 5.56% for Egypt; 0.9782, 26.41 W/m\({}^{2}\), 72.80 W/m\({}^{2}\), and 9.98% for Algeria. Figure 9 highlights the accuracy of the CNN-LSTM-ANN model when predicting solar radiation on an hourly time step for Algeria.  
Similar to West and Central Africa, the CNN-ANN hybrid model had the best predictive accuracy in comparison to the two other models (Table 4) for countries in the southern part of Africa. As seen in Figure 10, the CNN-ANN hybrid model can predict the hourly global solar radiation better than the ANN and CNN-LSTM-ANN models. The evaluation metrics (r-value, MAE, MAPE, and RMSE) for the two countries studied in this region are similar. This is a reflection of the uniform distribution of solar radiation in the region. While CNN-ANN had the best performance for this region, it is noteworthy that CNN-LSTM-ANN performance is also good for solar radiation prediction. However, ANN had the least accuracy of all the three models in this region. It should be noted that the performance of the models is compared over a period of 24 h out of the total 10,519 h test results. This is to highlight the intrinsic detailed differences in the models' predictive performance.