Generalized Extreme Gradient Boosting model for predicting daily global solar radiation for locations without historical data

Information on global solar radiation (R-s) is indispensable in many fields. However, reliable measurements of R-s are challenging worldwide because of high costs and technical complexities. Here, temperature-and sunshine based generalized Extreme Gradient Boosting (XGBoost) models were proposed to estimate daily R-s for locations where historical R-s data are unknown. Four combinations of input variables were assessed. The first two included: (1) maximum, minimum, mean, and diurnal temperature, and extra-terrestrial radiation (R-a); and (2) sunshine duration, maximum possible sunshine duration, and R-a. In the first two inputs, the latter two further included geographical variables, i.e., latitude, longitude, and altitude. The developed models were also compared with temperature-and sunshine-based generalized empirical models. Daily data of R-s, maximum and minimum temperature, and actual sunshine duration during the period of 2007-2016 from 96 radiation stations of China were collected to develop and evaluate the models. The results showed that accuracy of the generalized XGBoost models was improved when geographical variables were further included in various climate zones. The generalized XGBoost model using temperature and geographical data as inputs slightly reduced accuracy compared to the temperature-based local-trained XGBoost model but is still superior to the temperature-based generalized empirical model. Somewhat surprisingly, there was comparable performance between the generalized XGBoost model using sunshine and geographical data as inputs and the local-trained sunshine-based XGBoost model. Therefore, the generalized XGBoost model was highly recommended to estimate daily R-s incorporating sunshine/ temperature data and routinely available geographical information for locations where historical data are prior unknown.