Use of the artificial neural network and meteorological data for predicting daily global solar radiation in Djelfa, Algeria

This paper presents a set of artificial neural network models (ANN) to estimate daily global solar radiation (GSR) on a horizontal surface using meteorological variables: (mean daily extraterrestrial solar radiation intensity G₀, the maximum possible sunshine hours S₀, mean daily relative humidity H, mean daily maximum air temperature T, mean daily atmospheric pressure P and wind speed Vx) for Djelfa city in Algeria. In order to consider the effect of the different meteorological parameters on daily global solar radiation prediction, four following combinations of input features are considered: 1) Day of the year, G₀, S₀, T and Vx. 2) Day of the year, G₀, S₀, T, P and Vx. 3) Day of the year, G₀, S₀, T, H, P and Vx. 4) Day of the year, G₀, S₀, T, H and Vx. These models were compared using three evaluation criteria: Mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). The results show that the two parameters: atmospheric pressure and relative humidity affect the prediction output of global solar radiation. In addition, the results show that the relative humidity is the most important features influencing the prediction performance. It can be concluded that fourth model can be used for forecasting daily global solar radiation in other locations in Algeria.