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/m2, 46.84 W/m2, 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.
Highlights
The accurate forecast of the available renewable energy (RE) resources is evolving rapidly as this is one of the steps towards the maximization of RE potential
The second objective is to compare the performance of the hybrid neural network models with that of an artificial neural network model developed for the same purpose
When comparing the prediction model used for a single time series, or multiple time series with the same units, mean absolute error (MAE) and root mean square error (RMSE) are popular used to evaluate the performance of such models [48]
Summary
The accurate forecast of the available renewable energy (RE) resources is evolving rapidly as this is one of the steps towards the maximization of RE potential. The accurate forecasting of photovoltaic (PV) system production makes the use of solar energy more reliable [1]. The need to develop a model to predict systems’ monitoring and renewable energy resources prediction. The implementation of various forecast approaches for PV in microgrid and multigood demonstrated that PV systems can be used to operate. The continuous and accurate measurement of solar radiation over a long-term period makes the conversion and utilization of solar power more efficient [4]. The measurement of solar radiation is inadequate or unavailable for many (African and developing) countries [5]
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