Artificial Neural Networks Based Solar Radiation Estimation using Backpropagation Algorithm

Abstract

Being at the cutting edge for a long time, solar energy has found several applications in various areas. Optimal harvesting of solar energy is one of the thrust areas of the researchers and developers in 21 st century. Solar energy optimization solely depends on radiation received by the solar panels. Radiation is measured by various devices and it may be estimated by various estimation models. Solar energy needs to be estimated well in advance if the system to be designed has to be completely dependent on solar energy. This is a very challenging job since solar radiation depends on several parameters such as location changes and seasonal changes. Artificial Neural Network is one of the most preferred technique for the researchers in prediction related cases. This paper proposes a methodology to estimate solar radiation using feed forward back propagation neural network. A three-layer neural network is used here with one hidden layer. The data of 14 stations of Uttar Pradesh, India are obtained data from FAO, UN and it further divided into three sets of ‘Training’, Validation’ and ‘Testing’. This study is based on nine input parameters and one output parameter. Feed forward back propagation is used here to estimate solar radiation. The proposed model is validated for Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient training algorithms. The obtained results of Mean Square Error (MSE), Regression Values (R), Slope Values (m) and Intercept Values (c) in all three cases are justifying the suitability of the proposed model.