अमूर्त

Prediction of Global Solar Radiation Using Artificial Neural Network

Rashmi Jain, Bhawana Goel

Prediction of solar radiation is done by Artificial Neural Network (ANN) fitting tool. For training and testing the ANN solar radiation data of different cities with different climatic conditions are used. The analysis used is Levenberg-Marquard (LM) algorithm. The ANN model results are compared with measured data on the basis of root mean square error (RMSE) and mean bias error (MBE). For Indian region, it is found that RMSE in the ANN model varies 0.0486–3.562. For the study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict global solar radiation for different cities of India. As the input parameters various geographical, solar and meteorological parameters of different locations with diverse climatic conditions were used. The main objective of this study is to review Artificial Neural Network (ANN) based techniques to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that ANN techniques predict solar radiation more accurately in comparison to other (conventional) methods. We will found that the prediction accuracy of ANN models is dependent on input parameter combinations, training algorithm and architecture configurations.