Vol. 3, No. 3 (Summer 2016) 21-30   

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  Introducing the best model for predicting global solar radiation in Iran using empirical models and artificial neural network
H. Ghasemi Mobtaker, Y. Ajabshirchi, S. F. Ranjbar, M. Matloobi and M. Taki
( Received: January 10, 2016 – Accepted: September 04, 2016 )

Abstract    Precise knowledge of the amount of global solar radiation plays an important role in designing of a renewable energy systems. In this study, using long-term meteorological data, 19 empirical models were tested for prediction of monthly mean daily global solar radiation in Tabriz. Also various artificial neural networks (ANN) models were designed for the comparison with the empirical models. For this purpose, the meteorological data recorded by Iran Meteorological Office (1992–2013) was used. These data included: monthly mean daily sunshine duration, monthly mean ambient temperature, monthly mean maximum and minimum ambient temperature and monthly mean relative humidity. The results showed that the yearly average of solar radiation in the region was 16.37 MJ m-2 day-1. Among the empirical models, the best result was acquired for model (19) with correlation coefficient of 0.9663. Results also showed that the ANN model trained with total meteorological data in input layer produces better results than the others. RMSE and r for this model were 1.0800 MJ m-2 and 0.9714, respectively. Comparison between the two models demonstrated that modeling of monthly mean daily global solar radiation through the use of the ANN technique shows better estimates rather than the empirical models.


Keywords    Solar energy, Meteorological data, Sunshine hour, Prediction, Artificial neural networks.


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