## 4 Conclusions  
In this study, two novel deep learning models namely CNN-ANN and CNN-LSTM-ANN have been developed in Keras-Python and used to forecast hourly global solar radiation in Africa. The developed model is also compared to an ANN model developed for the same purpose. Data from ten different African countries within the five geopolitical zones have been used to train and test the models developed. Variation in weather conditions is one of the main factors that affect the performance of the model as seen in Table 4. The models performed better for locations with high and well-distributed solar radiation as in the case of Egypt, Chad, and Somalia. The integration of LSTM helps the model to retain long-term computational dependency thereby reducing the error term in comparison to ANN and this is obvious in the MAE results in Table 4. Additionally, the addition of CNN to the ANN algorithms helps in boosting the performance of the model thereby giving a more accurate or higher r-score (Table 4). The concluding remarks from this study are highlighted as follows. * The hybrid models were found to predict solar radiation more accurately than the ANN model. While CNN-ANN had the best performance for seven different countries (Ghana, Nigeria, Chad, CAR, Egypt, Namibia, and South Africa), CNN-LSTM-ANN had the best predictive performance for Algeria, Somalia, and Ethiopia. * Also, the integration of a flatten layer in the CNN-ANN hybrid model enhanced the predictive performance of this model. * The two-hybrid models train faster than the ANN model making it more desirable for computation in developing countries. * Out of all the countries considered in the scope of this study, the application of the CNN-ANN hybrid model in Chad had the overall best performance. The r-value, MAE, RMSE, and MAPE for this case are 0.9930, 15.70 W/m\({}^{2}\), 46.84 W/m\({}^{2}\), and 4.98 respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Research**} & \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \cline{3-6}  & & & **r** & **RMSE** & **MAPE** \\ \hline \multirow{3}{*}{[PERSON] et al. [50]} & \multirow{3}{*}{Nigeria} & SVM-FFA & 0.7280 & 1.8661 MJ/m\({}^{2}\) & 11.52\% \\  & & ANN & 0.6496 & 2.0458 MJ/m\({}^{2}\) & 13.43\% \\  & & GP & 0.6899 & 1.9532 MJ/m\({}^{2}\) & 13.21\% \\ \hline \multirow{3}{*}{[PERSON] et al. [52]} & \multirow{3}{*}{Spain} & EANN & 0.9598 & 85.71 W/m\({}^{2}\) & - \\  & & MLP & 0.9749 & 67.69 W/m\({}^{2}\) & - \\ \hline \multirow{3}{*}{[PERSON] et al. [53]} & \multirow{3}{*}{Turkey} & ANN & 0.9397 & 2.776 MJ/m\({}^{2}\) & 23.33\% \\  & & KNN & 0.9247 & 3.092 MJ/m\({}^{2}\) & 27.74\% \\  & & SVM & 0.9380 & 2.820 MJ/m\({}^{2}\) & 23.37\% \\ \hline \multirow{3}{*}{[PERSON] et al. [51]} & \multirow{3}{*}{China} & ANN & 0.9402 & 2.855 MJ/m\({}^{2}\)/day & - \\  & & MEA-ANN & 0.9407 & 2.814 MJ/m\({}^{2}\)/day & - \\  & & RF & 0.9284 & 3.103 MJ/m\({}^{2}\)/day & - \\  & & WNN & 0.9391 & 2.839 MJ/m\({}^{2}\)/day & - \\ \hline \multirow{3}{*}{[PERSON] et al. [49]} & \multirow{3}{*}{Algeria} & TM4 & 0.9111 & 2.3643 MJ/m\({}^{2}\) & - \\  & & TM5 & 0.8740 & 2.7879 MJ/m\({}^{2}\) & - \\  & & TM6 & 0.8954 & 2.5543 MJ/m\({}^{2}\) & - \\ \hline \multirow{3}{*}{Present study} & \multirow{3}{*}{Chad} & ANN & 0.9939 & 42.99 W/m\({}^{2}\) & 5.62\% \\  & & CNN-ANN & 0.9930 & 46.84 W/m\({}^{2}\) & 5.04\% \\  & & CNN-LSTM-ANN & 0.9921 & 49.08 W/m\({}^{2}\) & 4.98\% \\ \cline{1-1}  & & ANN & 0.9815 & 73.94 W/m\({}^{2}\) & 10.24\% \\ \cline{1-1}  & & CNN-ANN & 0.9827 & 71.35 W/m\({}^{2}\) & 9.45\% \\ \cline{1-1}  & & CNN-LSTM-ANN & 0.9830 & 71.03 W/m\({}^{2}\) & 10.76\% \\ \cline{1-1}  & & ANN & 0.9914 & 50.88 W/m\({}^{2}\) & 5.86\% \\ \cline{1-1}  & & Egypt & CNN-ANN & 0.9925 & 48.33 W/m\({}^{2}\) & 5.56\% \\ \cline{1-1}  & & CNN-LSTM-ANN & 0.9919 & 49.59 W/m\({}^{2}\) & 5.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hybrid model performance comparison with existing literature.