###### Abstract
Their results reflect that both ANN and RNN have good prediction accuracy for this purpose [17]. Furthermore, a comparative study of reliable ensemble learning-based models for solar prediction showed that ensemble models have a consistent and reliable prediction performance when applied to data from different locations [18]. [PERSON] et al. [19] proposed the use of a data-driven algorithm and contextual optimization for the forecasting of solar irradiance. Their approach achieved a consistent performance for long and short-term predictions for all the cities considered in their study. [PERSON] et al. [20] also used the neural network approach for the prediction of global solar irradiance using particle swarm optimization algorithm considering extremely short-time intervals. From their results, the three days performance profile for the model proposed in the study are 0.0292 of MSE, 0.7537 of MAE, 1.7078 of RMSE, and 31.4348 of MAPE (%) considering a 5 s time interval. Their model outperformed the existing standalone neural networks for solar irradiance prediction [20]. In another study, machine learning and deep learning models were compared for solar irradiance prediction and it was concluded that deep learning models are more viable for this specific forecasting task [21]. Other studies in existing literature [22; 23; 24] have worked on the forecast of solar radiation of which the use of ANN, RNN, and CNN models was proposed for different case studies. Owing to the under-development in Africa, models that will accurately predict solar radiation are required to encourage solar energy utilization in the continent. Africa as a continent has a high and well-distributed solar energy potential, however, this resource is underutilized and underdeveloped. The energy poverty and lack of access to electricity in many countries in Africa reflect the under-utilization of solar-powered systems. It is estimated that over 600 million Africans lack access to electricity [25]. Beyond Africa, the prediction of solar radiation has been studied in some literature [23], but there is still a need to develop more robust, accurate, and fast predictive models for solar radiation. The long short-term memory (LSTM) model retains long-term computational dependency thereby reducing the error term. Additionally, in comparison to other deep learning algorithms, the CNN algorithm is capable of excellently extracting nonlinear intrinsic features using a convolution process with pooling operations [26]. Consequently, if CNN models are applied in real energy or renewable energy resources forecasting, it will enhance the robustness of the energy design/resource for short-term or long-term forecasts which may be difficult to attain conventionally. To address the gaps in knowledge that advocate for a need to have versatile energy management devices that can boost the integration of solar energy (considering its variability in behavior), the novelty of this paper is to design a new hybrid deep learning forecast model based on the integration of LSTM, ANN, and CNN models. Two new hybrid neural network models namely, convolutional neural network/artificial neural network (CNN-ANN) and convolution neural network/long short-term memory/artificial neural network (CNN-LSTM-ANN) are developed for accurate solar radiation prediction in Africa. The first model (CNN-ANN) hybridized three hidden layers of CNN to extract the nonlinear intrinsic features of the data. Then a flatten layer, and an ANN model (with three hidden layers) in addition to an input and an output layer are integrated. Model-2 (CNN-LSTM-ANN) hybridizes two layers of CNN, one layer of LSTM, and three layers of ANN in its hidden layer. Although hybrid models such as LSTM-ANN [27] and LSTM-CNN [28] have been used to predict solar radiation and solar PV power production, no study in literature considered the use of the hybrid models presented in this research. This work has been bench-marked against the ANN model as this model is fundamental to the design of the hybrid models.