干旱区生态环境知识资源中心(Arid): Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods

Arid

DOI	10.1016/j.enconman.2020.113111
	Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods
	He, Chuan; Liu, Jiandong; Xu, Fang; Zhang, Teng; Chen, Shang; Sun, Zhe; Zheng, Wenhui; Wang, Runhong; He, Liang; Feng, Hao; Yu, Qiang; He, Jianqiang
通讯作者	He, JQ
来源期刊	ENERGY CONVERSION AND MANAGEMENT
ISSN	0196-8904
EISSN	1879-2227
出版年	2020
卷号	220
英文摘要	The values of global solar radiation are important fundamental data for potential evapotranspiration estimation, solar energy utilization, climate change study, crop growth model, and etc. This research tried to explore the optimal combination of input meteorological factors and the machine learning methods for the estimation of daily solar radiation under different climatic conditions so as to improve the estimation accuracy. Based on the correlation between meteorological factors, different meteorological factor input combinations were established and the support vector machine method was used to estimate global solar radiation at 80 weather stations in four climatic regions of China mainland. The results showed that, the optimal combinations of input meteorological factors were different in the four different climatic zones in China mainland. Three meteorological factors of sunshine hours, extraterrestrial radiation, and air temperature had greater impacts on the solar radiation estimation. Adding the factor of precipitation could obviously improve the estimation accuracy in humid regions, but not remarkably in arid regions. Wind speed had very little influence on solar radiation estimation. The accuracies of machine learning methods were better than the Angstrom-Prescott formula and the multiple linear regression method. Among them, support vector machine and extreme learning machine were more appropriate. In some sites, the root mean square error of support vector machine method was even 20% less than that of the Angstrom-Prescott formula. In general, reasonable division of the areas and establishment of appropriate input combinations of meteorological factors according to the climatic conditions, combined with machine learning methods, can effectively improve the accuracy of solar radiation estimation.
英文关键词	Solar radiation Machine learning Climatic zones Input combination Meteorological factor
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000563961500008
WOS关键词	SUPPORT VECTOR MACHINE ; SUNSHINE DURATION ; MEASURED TEMPERATURES ; EMPIRICAL-MODELS ; PREDICTION ; ENERGY ; SVM
WOS类目	Thermodynamics ; Energy & Fuels ; Mechanics
WOS研究方向	Thermodynamics ; Energy & Fuels ; Mechanics
来源机构	西北农林科技大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325893
作者单位	[He, Chuan; Feng, Hao; Yu, Qiang; He, Jianqiang] Northwest A&F Univ, Inst Water & Soil Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China; [He, Chuan; Liu, Jiandong] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China; [He, Chuan; Xu, Fang; Zhang, Teng; Chen, Shang; Sun, Zhe; Zheng, Wenhui; Wang, Runhong; He, Jianqiang] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid Area, Minist Educ, Yangling 712100, Shaanxi, Peoples R China; [Xu, Fang; Zhang, Teng; Chen, Shang; Sun, Zhe; Zheng, Wenhui; Wang, Runhong; Feng, Hao; He, Jianqiang] Northwest A&F Univ, Inst Water Saving Agr Arid Areas China, Yangling 712100, Shaanxi, Peoples R China; [He, Liang] Natl Meteorol Ctr, Beijing 100081, Peoples R China; [He, Jianqiang] Shaanxi Meteorol Bur, Key Lab Ecoenvironm & Meteorol Qinling Mt & Loess, Xian 710014, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	He, Chuan,Liu, Jiandong,Xu, Fang,et al. Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods[J]. 西北农林科技大学,2020,220.
APA	He, Chuan.,Liu, Jiandong.,Xu, Fang.,Zhang, Teng.,Chen, Shang.,...&He, Jianqiang.(2020).Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods.ENERGY CONVERSION AND MANAGEMENT,220.
MLA	He, Chuan,et al."Improving solar radiation estimation in China based on regional optimal combination of meteorological factors with machine learning methods".ENERGY CONVERSION AND MANAGEMENT 220(2020).