Modeling of Solar Radiation Potential in Iran Using Artificial Neural Networks

Abstract

Solar radiation data play an important role in sola r energy relevant researches. These data are not available for some locations due to th e absence of the meteorological stations. Therefore, solar radiation data have to be predicte d by using solar radiation estimation models. This study presents an integrated Artificia l Neural Network (ANN) approach for estimating solar radiation potential over Iran base d on geographical and meteorological data. For this aim, the measured data of 31 station s spread over Iran were used to train Multi-Layer Perceptron (MLP) neural networks with d ifferent input variables, and solar radiation was the output. The accuracy of the model s 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 re sults 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 capabilit y of the ANN model and its excellent ability to predict solar radiation in Iran. Keywords : ANN, Meteorological data, Multi non-linear regress ion, Solar radiation.