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

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 ven...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tang, Zhihua [verfasserIn] Yin, Hua [verfasserIn] Yang, Caiyun [verfasserIn] Yu, Junyan [verfasserIn] Guo, Huafang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression

Übergeordnetes Werk:	Enthalten in: Sustainable cities and society - Amsterdam [u.a.] : Elsevier, 2011, 66
Übergeordnetes Werk:	volume:66

DOI / URN:	10.1016/j.scs.2020.102690

Katalog-ID:	ELV005457556

Internformat


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520			\|a 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).
650		4	\|a Urban rail transit
650		4	\|a Energy consumption analysis
650		4	\|a Energy consumption prediction
650		4	\|a Support vector regression
650		4	\|a Binary nonlinear fitting regression
700	1		\|a Yin, Hua \|e verfasserin \|4 aut
700	1		\|a Yang, Caiyun \|e verfasserin \|4 aut
700	1		\|a Yu, Junyan \|e verfasserin \|4 aut
700	1		\|a Guo, Huafang \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Sustainable cities and society \|d Amsterdam [u.a.] : Elsevier, 2011 \|g 66 \|h Online-Ressource \|w (DE-627)635602792 \|w (DE-600)2573417-9 \|w (DE-576)336956703 \|7 nnns
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Indexfelder

author_variant	z t zt h y hy c y cy j y jy h g hg
matchkey_str	tangzhihuayinhuayangcaiyunyujunyanguohua:2020----:rdcigheetiiyosmtooubnalrnibsdniayolnaftiger
hierarchy_sort_str	2020
publishDate	2020
allfields	10.1016/j.scs.2020.102690 doi (DE-627)ELV005457556 (ELSEVIER)S2210-6707(20)30905-7 DE-627 ger DE-627 rda eng 690 720 DE-600 Tang, Zhihua verfasserin aut Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression Yin, Hua verfasserin aut Yang, Caiyun verfasserin aut Yu, Junyan verfasserin aut Guo, Huafang verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 66 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 66
spelling	10.1016/j.scs.2020.102690 doi (DE-627)ELV005457556 (ELSEVIER)S2210-6707(20)30905-7 DE-627 ger DE-627 rda eng 690 720 DE-600 Tang, Zhihua verfasserin aut Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression Yin, Hua verfasserin aut Yang, Caiyun verfasserin aut Yu, Junyan verfasserin aut Guo, Huafang verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 66 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 66
allfields_unstemmed	10.1016/j.scs.2020.102690 doi (DE-627)ELV005457556 (ELSEVIER)S2210-6707(20)30905-7 DE-627 ger DE-627 rda eng 690 720 DE-600 Tang, Zhihua verfasserin aut Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression Yin, Hua verfasserin aut Yang, Caiyun verfasserin aut Yu, Junyan verfasserin aut Guo, Huafang verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 66 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 66
allfieldsGer	10.1016/j.scs.2020.102690 doi (DE-627)ELV005457556 (ELSEVIER)S2210-6707(20)30905-7 DE-627 ger DE-627 rda eng 690 720 DE-600 Tang, Zhihua verfasserin aut Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression Yin, Hua verfasserin aut Yang, Caiyun verfasserin aut Yu, Junyan verfasserin aut Guo, Huafang verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 66 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 66
allfieldsSound	10.1016/j.scs.2020.102690 doi (DE-627)ELV005457556 (ELSEVIER)S2210-6707(20)30905-7 DE-627 ger DE-627 rda eng 690 720 DE-600 Tang, Zhihua verfasserin aut Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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). Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression Yin, Hua verfasserin aut Yang, Caiyun verfasserin aut Yu, Junyan verfasserin aut Guo, Huafang verfasserin aut Enthalten in Sustainable cities and society Amsterdam [u.a.] : Elsevier, 2011 66 Online-Ressource (DE-627)635602792 (DE-600)2573417-9 (DE-576)336956703 nnns volume:66 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 66
language	English
source	Enthalten in Sustainable cities and society 66 volume:66
sourceStr	Enthalten in Sustainable cities and society 66 volume:66
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression
dewey-raw	690
isfreeaccess_bool	false
container_title	Sustainable cities and society
authorswithroles_txt_mv	Tang, Zhihua @@aut@@ Yin, Hua @@aut@@ Yang, Caiyun @@aut@@ Yu, Junyan @@aut@@ Guo, Huafang @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	635602792
dewey-sort	3690
id	ELV005457556
language_de	englisch
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author	Tang, Zhihua
spellingShingle	Tang, Zhihua ddc 690 misc Urban rail transit misc Energy consumption analysis misc Energy consumption prediction misc Support vector regression misc Binary nonlinear fitting regression Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
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topic_title	690 720 DE-600 Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression Urban rail transit Energy consumption analysis Energy consumption prediction Support vector regression Binary nonlinear fitting regression
topic	ddc 690 misc Urban rail transit misc Energy consumption analysis misc Energy consumption prediction misc Support vector regression misc Binary nonlinear fitting regression
topic_unstemmed	ddc 690 misc Urban rail transit misc Energy consumption analysis misc Energy consumption prediction misc Support vector regression misc Binary nonlinear fitting regression
topic_browse	ddc 690 misc Urban rail transit misc Energy consumption analysis misc Energy consumption prediction misc Support vector regression misc Binary nonlinear fitting regression
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title	Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
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title_full	Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
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author_browse	Tang, Zhihua Yin, Hua Yang, Caiyun Yu, Junyan Guo, Huafang
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title_sort	predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
title_auth	Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
abstract	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).
abstractGer	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).
abstract_unstemmed	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).
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title_short	Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression
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author2	Yin, Hua Yang, Caiyun Yu, Junyan Guo, Huafang
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doi_str	10.1016/j.scs.2020.102690
up_date	2024-07-06T18:02:22.326Z
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Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression

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