Estimating Global Solar Radiation Using Artificial Neural Network and Climate Data in the South-western Region of Algeria

• Published in: Energy procedia 2012, Vol.18, p.531-537
• Main Authors: Hasni, Abdelhafid, Sehli, Abdelkrim, Draoui, Belkacem, Bassou, Abdesselam, Amieur, Belkacem
• Format: Article
• Language: English
• Subjects: Arid climate; Artificial neural networks; Global solar radiation; Relative Humidity; Temperature
• ISSN: 1876-6102; 1876-6102
• DOI: 10.1016/j.egypro.2012.05.064

Global solar radiation (GSR) data are desirable for many areas of research and applications in various engineering fields. However, GSR is not as readily available as air temperature data. Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to test an artificial neural network (ANN) for estimating the global solar radiation (GSR) as a function of air temperature and relative humidity data in a in the south-western region of Algeria. The measured data between 02 February to 31 May 2011 were used for training the neural networks while the remaining 651hours data from June 2011 as testing data. The testing data were not used in training the neural networks. The climatic data collected in weather station of Energy Laboratory in Drylands (ENERGARID) located in the south-western region of Algeria. Obtained results show that neural networks are well capable of estimating GSR from temperature and relative humidity. This can be used for estimating GSR for locations where only temperature and humidity data are available.