Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data

Abstract

In this study, solar radiation (SR) is estimated at 61 locations with varying climatic conditions using the artificial neural network (ANN) and extreme learning machine (ELM). While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature (LST) obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient (R-2), mean bias error (MBE), root mean square error (RMSE), and Willmott's index (WI) values were calculated as 0.943, -0.148 MJm(-2), 1.604 MJm(-2), and 0.996, respectively. While R-2 was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJm(-2), 0.672 MJm(-2), and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation.