干旱区生态环境知识资源中心(Arid): Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas

Arid

DOI	10.1016/j.renene.2021.05.099
	Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas
	Wu, Wei; Tang, Xiaoping; Lv, Jiake; Yang, Chao; Liu, Hongbin
通讯作者	Liu, HB (corresponding author), Southwest Univ, Coll Resources & Environm, Chongqing 400716, Peoples R China.
来源期刊	RENEWABLE ENERGY
ISSN	0960-1481
EISSN	1879-0682
出版年	2021
卷号	177 页码:148-163
英文摘要	This study aims to evaluate the potential of Bayesian additive regression trees (BART) for predicting global and diffuse solar radiation. Long-term daily weather data were collected at four stations in arid and humid areas. Models with different input combinations were created. The default parameters within R language package of BART were used. Model accuracy was assessed with Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative root mean square error (RRMSE), and Nash-Sutcliffe efficiency coefficient (NSE). Taylor diagram was applied to illustrate model performance. On average, the model with sunshine duration, theoretical sunshine duration, mean temperature, maximum temperature, minimum temperature, relative humidity, and rainfall performed best for predicting global solar radiation, with mean R of 0.973, RMSE of 1.685 MJ/m(2) d, NSE of 0.944, RRMSE of 0.124, and MAE of 1.265 MJ/m(2) d. The model with sunshine duration, theoretical sunshine duration, global solar radiation, extraterrestrial solar radiation, and day of year outperformed others for predicting diffuse solar radiation, with mean R of 0.912, RMSE of 1.291 MJ/m(2) d, NSE of 0.827, RRMSE of 0.214, and MAE of 0.933 MJ/m(2) d. The results showed that BART was a suitable method for predicting global and diffuse solar radiation using climatic variables. (C) 2021 Elsevier Ltd. All rights reserved.
英文关键词	Sunshine duration Seasonal variation Input combination Machine learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000683548000014
WOS关键词	SUPPORT VECTOR MACHINE ; EMPIRICAL-MODELS ; AIR-POLLUTION ; HORIZONTAL DIFFUSE ; SUNSHINE DURATION ; IRRADIANCE
WOS类目	Green & Sustainable Science & Technology ; Energy & Fuels
WOS研究方向	Science & Technology - Other Topics ; Energy & Fuels
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367834
作者单位	[Wu, Wei; Lv, Jiake] Southwest Univ, Coll Comp & Informat Sci, Chongqing 400716, Peoples R China; [Tang, Xiaoping] Shapingba Meteorol Bur, Chongqing 400030, Peoples R China; [Yang, Chao] Southwest Univ, Chongqing Tobacco Res Inst, Chongqing 400716, Peoples R China; [Liu, Hongbin] Southwest Univ, Coll Resources & Environm, Chongqing 400716, Peoples R China
推荐引用方式 GB/T 7714	Wu, Wei,Tang, Xiaoping,Lv, Jiake,et al. Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas[J],2021,177:148-163.
APA	Wu, Wei,Tang, Xiaoping,Lv, Jiake,Yang, Chao,&Liu, Hongbin.(2021).Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas.RENEWABLE ENERGY,177,148-163.
MLA	Wu, Wei,et al."Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas".RENEWABLE ENERGY 177(2021):148-163.