Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in...
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Autor*in:	Qing Tian [verfasserIn] Weihang Zhao [verfasserIn] Yun Wei [verfasserIn] Liping Pang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	RVFLNN thermal environment temperature prediction metro station

Übergeordnetes Werk:	In: Algorithms - MDPI AG, 2008, 11(2018), 4, p 49
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:4, p 49

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/a11040049

Katalog-ID:	DOAJ037933922

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520			\|a With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.
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allfieldsSound	10.3390/a11040049 doi (DE-627)DOAJ037933922 (DE-599)DOAJ3d95fe14b2474075827f200d04c25c5c DE-627 ger DE-627 rakwb eng T55.4-60.8 QA75.5-76.95 Qing Tian verfasserin aut Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station. RVFLNN thermal environment temperature prediction metro station Industrial engineering. Management engineering Electronic computers. Computer science Weihang Zhao verfasserin aut Yun Wei verfasserin aut Liping Pang verfasserin aut In Algorithms MDPI AG, 2008 11(2018), 4, p 49 (DE-627)581036506 (DE-600)2455149-1 19994893 nnns volume:11 year:2018 number:4, p 49 https://doi.org/10.3390/a11040049 kostenfrei https://doaj.org/article/3d95fe14b2474075827f200d04c25c5c kostenfrei http://www.mdpi.com/1999-4893/11/4/49 kostenfrei https://doaj.org/toc/1999-4893 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 4, p 49
language	English
source	In Algorithms 11(2018), 4, p 49 volume:11 year:2018 number:4, p 49
sourceStr	In Algorithms 11(2018), 4, p 49 volume:11 year:2018 number:4, p 49
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institution	findex.gbv.de
topic_facet	RVFLNN thermal environment temperature prediction metro station Industrial engineering. Management engineering Electronic computers. Computer science
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container_title	Algorithms
authorswithroles_txt_mv	Qing Tian @@aut@@ Weihang Zhao @@aut@@ Yun Wei @@aut@@ Liping Pang @@aut@@
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callnumber-first	T - Technology
author	Qing Tian
spellingShingle	Qing Tian misc T55.4-60.8 misc QA75.5-76.95 misc RVFLNN misc thermal environment misc temperature prediction misc metro station misc Industrial engineering. Management engineering misc Electronic computers. Computer science Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
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topic	misc T55.4-60.8 misc QA75.5-76.95 misc RVFLNN misc thermal environment misc temperature prediction misc metro station misc Industrial engineering. Management engineering misc Electronic computers. Computer science
topic_unstemmed	misc T55.4-60.8 misc QA75.5-76.95 misc RVFLNN misc thermal environment misc temperature prediction misc metro station misc Industrial engineering. Management engineering misc Electronic computers. Computer science
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title	Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
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title_full	Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
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title_sort	thermal environment prediction for metro stations based on an rvfl neural network
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title_auth	Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
abstract	With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.
abstractGer	With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.
abstract_unstemmed	With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.
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title_short	Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
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