Generalized Extreme Gradient Boosting model for predicting daily global solar radiation for locations without historical data. (15th April 2022)

• Record Type: Journal Article
• Authors: Qiu, Rangjian; Liu, Chunwei; Cui, Ningbo; Gao, Yang; Li, Longan; Wu, Zongjun; Jiang, Shouzheng; Hu, Meng
• Is Part Of: Energy conversion and management. Volume 258(2022)
• Publication Date: 2022-04-15
• Subjects: Generalized XGBoost model -- Global solar radiation -- Empirical model -- Temperature-based -- Sunshine duration -- Local-trained model; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115488
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Generalized XGBoost models for predicting daily solar radiation were developed. Accuracy of generalized XGBoost models was improved if geographical data were included. Generalized XGBoost model using S and geographical data was comparable to local-trained. Generalized XGBoost models were superior to empirical ones. Abstract: Information on global solar radiation ( Rs ) is indispensable in many fields. However, reliable measurements of Rs 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 Rs for locations where historical Rs 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 ( Ra ); and (2) sunshine duration, maximum possible sunshine duration, and Ra . 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 Rs, 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 Rs incorporating sunshine/temperature data and routinely available geographical information for locations where historical data are prior unknown.