Forecasting solar radiation is fundamental to several domains related to renewable energy where several methods have been used to predict daily solar radiation, such as artificial intelligence and hybrid models. Recently, the Gaussian process regression (GPR) algorithm has been used successfully in remote sensing and Earth sciences. In this paper, a wavelet-coupled Gaussian process regression (W–GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). For this purpose, 3 years of data (2013–15) have been used in model training while the data of 2016 were used to validate the model. In this work, different types of mother wavelets and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid W–GPR model compared with the classical GPR model in terms of root mean square error (RMSE), relative root mean square error (rRMSE), mean absolute error (MAE) and determination coefficient (R2).

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Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).