标题：Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China作者：Zou, Ling; Wang, Lunche; Lin, Aiwen; Zhu, Hongji; Peng, Yuling; Zhao,Zhenzhen

来源出版物：JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS卷：146 页码：110-122 DOI: 10.1016/j.jastp.2016.05.013 出版年：AUG 2016

摘要：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 Angstrom-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 ( 5.21%) between the measured and ANN-estimated GSR. The statistical errors of RMSE (RMSE %) are 0.91 MJ 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 R2 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.

入藏号：WOS:000380593300011

文献类型：Article

语种：English

作者关键词：Artificial neural network, Spatial interpolation, Empirical models, Global solar radiation

扩展关键词：METEOROLOGICAL DATA; SUNSHINE DURATION; COMPLEX TERRAIN; AIR-TEMPERATURE; HYBRID MODEL; SURFACE; PRECIPITATION; SIMULATION; HUMIDITY; COEFFICIENTS

通讯作者地址：Lin, AW (reprint author), Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei Province, Peoples R China.

电子邮件地址：aiwen_lin@163.com

地址：

[Zou, Ling; Lin, Aiwen; Zhu, Hongji; Peng, Yuling; Zhao, Zhenzhen] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei Province, Peoples R China.

[Wang, Lunche] China Univ Geosci, Dept Geog, Sch Earth Sci, Wuhan 430074, Hubei Province, Peoples R China.

研究方向：Geochemistry & Geophysics; Meteorology & Atmospheric Sciences

ISSN：1364-6826

eISSN: 1879-1824

影响因子：1.463