Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models

Kim, Minjeong; Sankararao, B.; Kang, OnYu; Kim, JeongTai; Yoo, ChangKyoo

doi:10.1016/j.enbuild.2011.10.047

Cited by 83 publications

(39 citation statements)

References 28 publications

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“…The new system should also take into account key inputs such as indoor air quality [29,30] and temperature readings. Preliminary studies show that a saving of up to 30% could be achieved.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

A breakdown of energy consumption in an underground station

Casals

Gangolells

Forcada

et al. 2014

Energy and Buildings

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Underground transportation systems are big energy consumers and have significant impacts on energy consumption at a regional scale. The literature has revealed that the energy consumption for non-traction purposes may be of the same magnitude as the energy used to move rolling stock, and in some cases even greater. However, most of the research conducted so far has focused on the energy demand of rolling stock. This paper investigates the electricity consumption of an underground metro station using data from on-site surveys and measurements. With an average consumption of 217.64 kWh/m(2)/year, the breakdown revealed that the lighting system dominated the underground station's energy consumption (37%). Illuminated advertising signs were found to be responsible for 14% of the total energy consumption, and ventilation accounted for another 14%. The rest of the energy consumption was attributed to systems such as mobile phone signal antenna (12%), the vertical transportation system (8%) and small power devices (5%). Accurate information on energy consumption for non-traction usage is useful for future implementation of energy conservation measures in underground stations, which could result in a reduction of operating costs in the long run.Peer ReviewedPostprint (author's final draft

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“…The new system should also take into account key inputs such as indoor air quality [29,30] and temperature readings. Preliminary studies show that a saving of up to 30% could be achieved.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

A breakdown of energy consumption in an underground station

Casals

Gangolells

Forcada

et al. 2014

Energy and Buildings

View full text Add to dashboard Cite

show abstract

“…MLR has been used for predicting PM10 concentrations in ambient air [33]. Recently, MLR has also been used for PM10 in subways [24,30,31]. The equation for the general MLR is:…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

“…There have recently been a number of studies on IAQ in subways, but a limited amount of research work on the IAQ prediction for real time ventilation control [1,2,3,[23][24][25][26][27][28][29][30][31]. Among them, a research team at Kyung Hee University has been most actively and comprehensively carrying out various analysis and model development studies on IAQ in subways for the last few years [24][25][26][27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

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A Practical Approach to the Real Time Prediction of PM₁₀for the Management of Indoor Air Quality in Subway Stations

Jeong¹,

Lee²

2016

The Transactions of The Korean Institute of Electrical Engineer

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-The real time IAQ (Indoor Air Quality) management is very important for large buildings and underground facilities such as subways because poor IAQ is immediately harmful to human health. Such IAQ management requires monitoring, prediction and control in an integrated and real time manner. In this paper, we present three PM10 hourly prediction models for such realtime IAQ management as both Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. Both MLR and ANN models show good performances between 0.76 and 0.88 with respect to R (correlation coefficient) between the measured and predicted values, but the MLR models outperform the corresponding ANN models with respect to RMSE (root mean square error).

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“…Yu et al proposed a wireless sensor network system to monitor carbon dioxide concentration [4]. Seasonal models for monitoring and predicting IAQ in subway or metro systems were developed by Kim et a.l [5].…”

Section: Introductionmentioning

confidence: 99%

An E-Nose-based indoor air quality monitoring system: prediction of combustible and toxic gas concentrations

Mumyakmaz¹,

Karabacak²

2015

Turk J Elec Eng & Comp Sci

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Abstract:A system for monitoring and predicting indoor air quality level is proposed in this paper. The system comprises a computer with a monitoring program and a sensor cell, which has an array of metal oxide gas sensors along with a temperature and humidity sensor. The gas sensors in the cell have been chosen to detect only hydrogen, methane, and carbon monoxide gases. Methane was selected as a representative for indoor combustible gases, and carbon monoxide was used to represent indoor toxic gases. Hydrogen was used as an interfering (and also combustible) gas in the study.A number of experiments were conducted to train the three artificial neural networks of the monitoring system. The networks have been trained using 80% of the gathered data with the Levenberg-Marquardt algorithm. The results of this work show that the performance rate of the proposed monitoring system in determining gas type for the limited sample space is 100% even when there is an interfering gas such as hydrogen in the environment. The trained system can predict the concentration level of the methane and carbon dioxide gases with a low absolute mean percent error rate of almost 1%.

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Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models

Cited by 83 publications

References 28 publications

A breakdown of energy consumption in an underground station

A breakdown of energy consumption in an underground station

A Practical Approach to the Real Time Prediction of PM₁₀for the Management of Indoor Air Quality in Subway Stations

An E-Nose-based indoor air quality monitoring system: prediction of combustible and toxic gas concentrations

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Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models

Cited by 83 publications

References 28 publications

A breakdown of energy consumption in an underground station

A breakdown of energy consumption in an underground station

A Practical Approach to the Real Time Prediction of PM10for the Management of Indoor Air Quality in Subway Stations

An E-Nose-based indoor air quality monitoring system: prediction of combustible and toxic gas concentrations

Contact Info

Product

Resources

About

A Practical Approach to the Real Time Prediction of PM₁₀for the Management of Indoor Air Quality in Subway Stations