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

文献类型：期刊论文

作者Tang, Zhihua1,2; Yin, Hua1,2; Yang, Caiyun1,2; Yu, Junyan3; Guo, Huafang1,2
刊名SUSTAINABLE CITIES AND SOCIETY
出版日期2021-03-01
卷号66页码:14
关键词Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression
ISSN号2210-6707
DOI10.1016/j.scs.2020.102690
通讯作者Yin, Hua(yinhua@ms.giec.ac.cn)
英文摘要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).
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
语种英语
WOS记录号WOS:000630929900001
出版者ELSEVIER
资助机构Natural Science Foundation of Guangdong Province
源URL[http://ir.giec.ac.cn/handle/344007/32797]  
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.Guangdong Key Lab New & Renewable Energy Res & De, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China
2.Chinese Acad Sci, Guangzhou Inst Energy Convers, Key Lab Renewable Energy, 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
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.

入库方式： OAI收割

来源：广州能源研究所

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