Accueil > VALORISATION > Rеvuе des Enеrgies Renouvеlablеs > Numéros Spéciaux > "SIENR"18 - Ghardaïa 2018" > New predictive model of hourly global solar component

New predictive model of hourly global solar component

S. Belaid 1, A. Boualit 1, M. Zaiani 1 and A. Mellit 2

1 Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaia, Algeria.
2 Renewable Energy Laboratory, Jijel University, 18000 Jijel, Algeria.


A prior knowledge of solar energy available on the studied site is necessary, to make the best use of the energy production of different solar installations. For this, several predictive techniques are available in the literature. In this work, new predictive model is proposed using support vector machine (SVM) with the principle of simple and partial autocorrelations, to predict the hourly global solar radiation (HGSR) at one hour step ahead. Two approaches have been adopted ; the first by considering five meteorological parameters as inputs for the model at past time (Hour-1 and Hour-2) to predict the future HGSR (Hour H) and the second one is directly inspired from the time series principle. From all the developed models, accurate predictions were obtained with the second approach with order of two in autocorrelation. His performances are such as mean absolute percentage error (MAPE) is equal to 19.72 %, correlation coefficient R = 99 % and normalized root mean square error NRMSE= 13.08 %. As a final task, the predictive performances of this selected model are compared with those of some ones cited in the literature based on other methodologies. This comparison is just to prove the ability of SVM technique in prediction of hourly global solar radiation time series.


Hourly solar component - Prediction - SVM - Autocorrelations - Time series.

New predictive model of hourly global solar component
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