###### Abstract  
There are a lot of developing countries with inadequate meteorological stations to measure solar radiation. This has been a major drawback for solar power applications in these countries as the performance of the solar-powered system cannot be accurately forecasted. In this study, two novel hybrid neural networks namely, convolutional neural network/artificial neural network (CNN-ANN) and convolutional neural network/long short-term memory/artificial neural network (CNN-LSTM-ANN), have been developed for hourly global solar radiation prediction. ANN models are also developed and the performance of the hybrid neural network models is compared with it. This study contributes to the search for more accurate solar radiation estimation methods. The hybrid neural network models are trained/tested with data from ten different countries across Africa. Results from this study indicate that the performance of all the hybrid models developed in this study is superior to what has been presented in existing literature with their r values ranging from 0.9662 to 0.9930. CNN-ANN model is the best for solar radiation forecasting in Southern, Central, and West Africa. CNN-LSTM-ANN is better for East Africa while both CNN-ANN and CNN-LSTM-ANN are suitable for North Africa. CNN-ANN application for solar radiation prediction in Chad had the overall best performance with an r-value, MAE, RMSE, and MAPE of 0.9930, 15.70 W/m\({}^{2}\), 46.84 W/m\({}^{2}\), and 4.98% respectively. The integration of CNN and LSTM algorithms with an ANN model enhanced long-term computational dependency and reduce error terms for the model. artificial intelligence; convolutional neural network; artificial neural network; recurrent neural network; solar radiation +
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of solar radiation over a long-term period makes the conversion and utilization of solar power more efficient [4]. However, the measurement of solar radiation is inadequate or unavailable for many (African and developing) countries [5]. In Nigeria (the largest economy and the most populated country in Africa), there are only 54 stations for measuring solar irradiance instead of the required 9000 stations [6]. This is the reality of most African/developing countries. The greenhouse effect and depletion of fossil fuels have concentrated recent research attention on RE resources utilization thereby increasing the demand for solar power [7]. The accurate forecasting of solar PV power generation is important for the following reasons [8]: (a) Accurate solar irradiance prediction can improve solar power utilization, thereby reducing economic losses from electrical restrictions. (b) Solar PV power generation is random, volatile, and intermittent creating a reliability problem in the power grid. Hence, the accurate prediction of solar irradiation can increase solar PV integration and improve the reliability of the power grid. These reasons have made solar irradiance and solar power generation an important subject in the energy research field [9]. In recent years, forecasting techniques such as time-series model-based techniques [10], physical methods, ensemble methods [11], and mapping techniques have been used for different prediction purposes [12]. Furthermore, the use of random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and other deep learning models have been considered for different RE resources prediction as well as electricity load forecast [13]. A review of different studies on the direct prediction of PV power reported that ANN and SVM models perform well under rapid and varying environmental conditions [14]. A short-term PV power forecast with the GA-SVM hybrid model showed that hybrid models are more robust, accurate, and they require less memory [15]. The adaptative neuro-fuzzy approach was used by [PERSON] et al. [16] for solar radiation prediction in Nigeria while [PERSON] et al. [17] studied the use of RNN and ANN for solar radiation prediction in Alabama.