Artificial neural networks based prediction of hourly horizontal solar radiation data: case study Online publication date: Thu, 18-May-2017
by Chaba-Mouna Siham; Hanini Salah; Laidi Maamar; Khaouane Latifa
International Journal of Applied Decision Sciences (IJADS), Vol. 10, No. 2, 2017
Abstract: The aim of the present study is to predict global solar radiation (GSR) received on the horizontal surface using artificial neural network (ANN). The measured data of the year (2013) was provided by the Applied Research Unit of Ghardaia - Algeria. The best results were obtained with a 7/24/1 ANN model trained with the quasi-Newton back propagation (BFGS) algorithm. The prediction accuracy for the internal and the external validation set was estimated by the Q2LOO and Q2ext which are equal to 0.9984, 0.9977 for ANN, with percent root mean square error (PRMSE) of 4.71% and the mean bias error (MBE) 0.021% for the internal validation and 5.60%, 0.42% for the external validation, respectively. These results show that the optimised model is robust and have a good predictive power explained by a good agreement between the measurement and prediction values of the solar radiation.
Online publication date: Thu, 18-May-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Decision Sciences (IJADS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org