Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression

doi:10.1016/j.scs.2020.102690

GIEC OpenIR

	Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
	Tang, Zhihua 1,2; Yin, Hua1,2 ; Yang, Caiyun 1,2; Yu, Junyan 3; Guo, Huafang1,2
	2021-03-01
发表期刊	SUSTAINABLE CITIES AND SOCIETY
ISSN	2210-6707
卷号	66 页码:14
通讯作者	Yin, Hua(yinhua@ms.giec.ac.cn)
摘要	Predicting the energy consumption of urban rail transit is conducive to reducing energy consumption in the subway system. Therefore, binary nonlinear fitting regression (BNFR) and support vector regression (SVR) models are developed to predict total electricity, traction electricity, and heating ventilation air conditioning (HVAC) system electricity consumption in subway lines as well as the electricity consumption of chillers in a subway station. The two models are compared in terms of accuracy, and the results demonstrate that the SVR model is superior to the BNFR model. The prediction accuracies of traction electricity and total electricity consumption in subway lines are high. By contrast, the prediction accuracy of the HVAC system electricity consumption in subway lines is low. This is due to numerous factors aside from outdoor temperature, operation mileages, and passenger flow, which can influence the HVAC system electricity. Thus, the influencing factors should further be investigated to increase the prediction accuracy. The electricity consumption of chillers in subway station can be predicted with the comprehensive consideration of indoor and outdoor temperatures and humidity levels, passenger flows, timetable of trains, power of new draught fans, and chilling of strong electricity rooms (SERs).
关键词	Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression
DOI	10.1016/j.scs.2020.102690
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of Guangdong Province[2018A030310084]
WOS研究方向	Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels
项目资助者	Natural Science Foundation of Guangdong Province
WOS类目	Construction & Building Technology ; Green & Sustainable Science & Technology ; Energy & Fuels
WOS记录号	WOS:000630929900001
出版者	ELSEVIER
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.giec.ac.cn/handle/344007/32798
专题	中国科学院广州能源研究所
通讯作者	Yin, Hua
作者单位	1.Chinese Acad Sci, Guangzhou Inst Energy Convers, Key Lab Renewable Energy, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China 2.Guangdong Key Lab New & Renewable Energy Res & De, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China 3.Guangzhou Metro Grp Co Ltd, Tower A,Wansheng Sq,1238 Xingang East Rd, Guangzhou 510330, Peoples R China
第一作者单位	中国科学院广州能源研究所
推荐引用方式 GB/T 7714	Tang, Zhihua,Yin, Hua,Yang, Caiyun,et al. Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression[J]. SUSTAINABLE CITIES AND SOCIETY,2021,66:14.
APA	Tang, Zhihua,Yin, Hua,Yang, Caiyun,Yu, Junyan,&Guo, Huafang.(2021).Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression.SUSTAINABLE CITIES AND SOCIETY,66,14.
MLA	Tang, Zhihua,et al."Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression".SUSTAINABLE CITIES AND SOCIETY 66(2021):14.