Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters

This paper evaluates the accuracy of Support Vector Machine (SVM), Artificial Neural Network (ANN) and empirical solar radiation models with different combination of input parameters. The parameters include month, latitude, longitude, bright sunshine hours, day length, relative humidity, maximum and minimum temperature. The models are evaluated based on statistical measures. Four new empirical models are introduced and validated with experimental data. This work is focused on the prediction of monthly mean daily global solar radiation (GSR) for different cities in India with most influencing input parameters identified using Waikato Environment for Knowledge Analysis (WEKA) software. WEKA identifies month, latitude, maximum temperature and bright sunshine hours as the most influencing and relative humidity as the least influencing input parameter. SVM model with most influencing input parameter performs better than ANN and Empirical models. Exclusion of relative humidity does not affect the prediction accuracy. Therefore this work reduces the dimensionality of the data and improves the prediction accuracy. This work also attempts in assessing the solar energy potential of smart cities of Tamil Nadu, India using the SVM model. The predicted annual GSR varies from 17 to 22 MJ/m2/day which is precise enough for a wide range of solar applications.