Modeling Solar Energy Potential in a Tehran Province Using Artificial Neural Networks

Abstract

Prediction of daily global solar radiation (GSR) plays an important role in design of renewable energy systems. Artificial Neural Networks (ANNs) are powerful tools for modeling and estimating GSR even though using few inputs. In order to train the networks, a dataset of meteorological daily time series for 15 years (1993–2008) collected in Tehran by Iran Meteorological Office were used. The meteorological parameters used to estimate GSR were daily values of maximum, minimum, and mean temperatures; relative humidity; sunshine duration; and precipitation as inputs and the daily GSR in MJ m−2 day−1 as output. Various ANN models were designed and implemented by combining different meteorological data. The optimum model for estimating GSR had one hidden layer multilayer perceptron (MLP) with 37 neurons in it when the inputs were number of the maximum and minimum temperature, sunshine duration, daylight hours, extraterrestrial radiation, and number of day in the year. The empirical Hargreaves and Samani equation (HS) was also considered for the comparison. To estimate the difference between measured and estimated values of ANN and empirical models, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (r) were determined. For 6-37-1 topology, r, RMSE, and MAE values were found to be 0.968, 3.09, and 2.57, respectively. Obtained results showed that ANN model outperformed HS model and can be successfully used for estimating the daily GSR for Tehran province and any other location.