Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China. (August 2016)

• Record Type: Journal Article
• Authors: Zou, Ling; Wang, Lunche; Lin, Aiwen; Zhu, Hongji; Peng, Yuling; Zhao, Zhenzhen
• Is Part Of: Journal of atmospheric and solar-terrestrial physics. Volume 146(2016)
• Page Start: 110
• Page End: 122
• Publication Date: 2016-08
• Subjects: Artificial neural network -- Spatial interpolation -- Empirical models -- Global solar radiation; Geophysics -- Periodicals; Atmospheric physics -- Periodicals; Géophysique -- Périodiques; Météorologie physique -- Périodiques; Electronic journals; 551.51
• DOI: 10.1016/j.jastp.2016.05.013
• Languages: English
• ISSNs: 1364-6826

Abstract

Abstract: Solar radiation plays important roles in energy application, vegetation growth and climate change. Empirical relations and machine-learning methods have been widely used to estimate global solar radiation (GSR) in recent years. An artificial neural network (ANN) based on spatial interpolation is developed to estimate GSR in southeast China. The improved Bristow–Campbell (IBC) model and the improved Ångström–Prescott (IA–P) model are compared with the ANN model to explore the best model in solar radiation modeling. Daily meteorological parameters, such as sunshine duration hours, mean temperature, maximum temperature, minimum temperature, relative humidity, precipitation, air pressure, water vapor pressure, and wind speed, along with station-measured GSR and a daily surface GSR dataset over China obtained from the Data Assimilation and Modeling Center for Tibetan Multi-spheres (DAM), are used to predict GSR and to validate the models in this work. The ANN model with the network of 9-17-1 provides better accuracy than the two improved empirical models in GSR estimation. The root-mean-square error (RMSE), mean bias error (MBE), and determination coefficient ( R 2 ) are 2.65 MJ m −2, −0.94 MJ m −2, and 0.68 in the IA−P model; 2.19 MJ m −2, 1.11 MJ m −2, and 0.83 in the IBC model; 1.34 MJ m −2, −0.11 MJ m −2, and 0.91 in the ANN model, respectively. The regional monthly mean GSR in the measured dataset, DAM dataset, and ANN model is analyzed. The RMSE (RMSE %) is 1.07 MJ m −2 (8.91%) and the MBE (MBE %) is −0.62 MJ m −2 (−5.21%) between the measured and ANN-estimated GSR. The statistical errors of RMSE (RMSE %) are 0.91 MJ m −2 (7.28%) and those of MBE (MBE %) are −0.15 MJ m −2 (−1.20%) between DAM and ANN-modeled GSR. The correlation coefficients and R 2 are larger than 0.95. The regional mean GSR is 12.58 MJ m −2 . The lowest GSR is observed in the northwest area, and it increases from northwest to southeast. The annual mean GSR decreases by 0.02 MJ m −2 decade −1 over the entire southeast China. The GSR in 52 stations experiences a decreasing trend, and 21% of the stations are significant at the 95% level. Highlights: The IBC model and IA–P model are compared with spatial interpolation based ANN to explore the best model in GSR modeling. The ANN model provides better accuracy than the two improved empirical models in GSR estimation. Good agreements are observed between ANN-modeled GSR and sites-measured/DAM-modeled values. A long-term decrease of global solar radiation is observed in southeast China.