Prediction of Solar Radiation in Bushehr Using Artificial Neural Networks

Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, and manufacturing, and optimization, signal processing and social/psychological sciences. They are particularly useful in system modeling such as in implementingcomplex mappings and system identification. In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Bushehr (lat. 27–30 N, log. 50–52 E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Meteorological data of Bushehr in Iran for period of 1 years from the Bushehr Meteorological Organization were used for the training and testing the network. Meteorological data (dew point, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual global solar radiation intensities for training and testing datasets were higher than 98%, thus suggesting a high reliability of the model for evaluation of solar radiation in Bushehr. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

کلیدواژه ها:

Artificial neural network ، Renewableenergy ، Solar radiation ، Multi-layer perceptron