Estimation of Daily Average Global Solar Radiation with Nonlinear Regression Models Developed Using Some Meteorological and Geographical Parameters

Abstract

In this study, it is aimed to develop empirical models that can be used in estimation of daily average solar radiation (RS) based on some meteorological and geographical parameters. Seven estimation models were developed by nonlinear regression analysis method using various combinations of air temperature (T), relative humidity (RH), extraterrestrial radiation (Ra), saturated (es) and actual vapour pressure (ea) parameters. The models were created using the long-term average daily meteorological data of Kahramanmaraş province (1938 – 2020). The models were tested both these long-term average data and daily meteorological data measured at Kahramanmaraş Sütçü İmam University (KSU) in 2019 and 2020. Long-term average daily actual RS data varied between 4.99 – 32.56 MJ m-2 day-1. The estimated solar radiation values (("RS" ) ̂) with the highest correlation (r = 0.99) with actual RS data were obtained with the RS_7 model, in which the parameters es, ea, T, RH and Ra were used together. The ("RS" ) ̂ values obtained using this model varied between 6.45 to 33.99 MJ m-2 day-1. For the RS_7, which showed the best performance among the seven models, mean absolute percentage error (MAPE) and root mean square error (RMSE) were determined as 4.17% and 0.69 MJ m-2 day-1, respectively. The daily RS values measured in KSU varied between 7.75 – 33.48 MJ m-2 day-1 and 10.51 – 30.23 MJ m-2 day-1 for 2019 and 2020. The ("RS" ) ̂ values closest to the measured RS values were estimated with the RS_7 model. The estimated ("RS" ) ̂ values by this model varied between 11.74 – 33.93 MJ m-2 day-1 and 13.93 – 31.57 MJ m-2 day-1 for 2019 and 2020, respectively. MAPE values were determined as 11.33% and 7.54%, respectively. It is concluded that this model can be used to estimates daily average solar radiation and will be an excellent alternative since it is compatible with the Kahramanmaraş conditions.