Gorjian, Sh., Ghobadian, B., Tavakkoli Hashjin, T. (2015). Modeling of Solar Radiation Potential in Iran Using Artificial Neural Networks. , 17(7), 1707-1723.
Sh. Gorjian; B. Ghobadian; T. Tavakkoli Hashjin. "Modeling of Solar Radiation Potential in Iran Using Artificial Neural Networks". , 17, 7, 2015, 1707-1723.
Gorjian, Sh., Ghobadian, B., Tavakkoli Hashjin, T. (2015). 'Modeling of Solar Radiation Potential in Iran Using Artificial Neural Networks', , 17(7), pp. 1707-1723.
Gorjian, Sh., Ghobadian, B., Tavakkoli Hashjin, T. Modeling of Solar Radiation Potential in Iran Using Artificial Neural Networks. , 2015; 17(7): 1707-1723.

Modeling of Solar Radiation Potential in Iran Using Artificial Neural Networks

Journal of Agricultural Science and Technology
Article 5, Volume 17, Issue 7, 2015, Pages 1707-1723    PDF (1.89 M)
Authors
Sh. Gorjian; B. Ghobadian* ; T. Tavakkoli Hashjin
Department of Biosystems Engineering, Faculty of Agriculture, Tarbiat Modares University (T.M.U.), Tehran, Iran. P.O. Box 14115-111.
Abstract
Solar radiation data play an important role in solar energy relevant researches. These data are not available for some locations due to the absence of the meteorological stations. Therefore, solar radiation data have to be predicted by using solar radiation estimation models. This study presents an integrated Artificial Neural Network (ANN) approach for estimating solar radiation potential over Iran based on geographical and meteorological data. For this aim, the measured data of 31 stations spread over Iran were used to train Multi-Layer Perceptron (MLP) neural networks with different input variables, and solar radiation was the output. The accuracy of the models was evaluated using the statistical indicators of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R); hence, the best model in each category was identified. The Stepwise Multi NonLinear Regression (MNLR) method was used to determine the most suitable input variables. The results obtained from the ANN models were compared with the measured data. The MAPE and RMSE were found to be 2.98% and 0.0224, respectively. The obtained R value was about 99.85% for the testing data set. The results testify to the generalization capability of the ANN model and its excellent ability to predict solar radiation in Iran.
Keywords
ANN; Meteorological data; Multi non-linear regression; Solar radiation
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