Data Normalisation-Based Solar Irradiance Forecasting Using Artificial Neural Networks

Abstract

Due to continual day-to-day increase in electricity demand, and hazardous and critical threats of fossil fuels to the environment, researchers are scrutinizing over substitute energy sources. Solar radiation intensity prediction is essential for conducting various research work in the emerging field of Renewable Energy Sources (RESs). This paper has presented development of monthly averaged solar radiation intensity prediction model by employing Artificial Neural Network (ANN) algorithm. Various meteorological parameters have been considered over period of 2 years to execute forecasting for Chandigarh, India. Different normalisation techniques such as min-max, decimal and z-score have been utilised to normalise database. Structure and parameter learning of ANNs has been carried out. Comparative analysis has been done to select optimal architecture based on different performance evaluation measures such as mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and correlation coefficient (R-value) and training time. The network topology with least forecasting errors, higher R-value has been found to be optimum and further simulated for predicting monthly averaged solar radiation intensity for Chandigarh region.