Prediction of hourly and daily diffuse solar fraction in the city of Fez (Morocco)

Abstract

In this paper, 3-layers MLP (Multi-Layers Perceptron) Artificial Neural Network (ANN) models have been developed and tested for predicting hourly and daily diffuse solar fractions at Fez city in Morocco. In parallel, some empirical models were tested. Three years of data (2009–2011) have been used for establishing the parameters of all tested models and 1 year (2012) to test their prediction performances. To select the best ANN (3-layers MLP) architecture, we have conducted several tests by using different combinations of inputs and by varying the number of neurons in the hidden layer. The output is only the diffuse solar fraction. The performances of each model were assessed on the basis of four statistic characteristics: mean absolute error (MAE), relative mean bias error (RMBE), relative root mean square error (RRMSE) and the degree of agreement (DA). Additionally, the coefficient of correlation (R) is used to test the linear regression between predicted and observed data. The results indicate that the ANN model is more suitable for predicting diffuse solar fraction than the empirical tested models at Fez city in Morocco.