Automatic ventilation control algorithm considering the indoor environmental quality factors and occupant ventilation behavior using a logistic regression model

Hakpyeong Kim, Taehoon Hong, Jimin Kim

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Building occupants find it difficult to achieve the optimal indoor environment quality (IEQ) through natural ventilation. To solve this problem, this study aimed to develop an automatic ventilation control algorithm considering the IEQ factors and occupant ventilation behavior. The algorithm was developed in four steps: (i) real-time collection of data on the IEQ factors and occupant ventilation behavior; (ii) development of the automatic ventilation control algorithm using logistic regression; (iii) determination of the automatic ventilation control algorithm using receiver operating characteristic curve analysis; and (iv) evaluation of the automatic ventilation control algorithm's performance according to the indoor environmental standards. Through this process, the logistic regression model with ridge regression (area under curve: 0.865), with the highest classification accuracy, was selected. Then Youden's index was used to define the decision criterion (i.e., optimal cutoff value) for the logistic regression model. As a result, the decision criterion for opening and closing the windows or doors was 0.533. When the developed algorithm was compared with the indoor environmental standards to analyze its performance, the compliance rate of the opening of the windows or doors based on the monitored data was 77.6%, but it increased to 99% based on the data classified by the developed algorithm. It is expected that if the automatic ventilation control algorithm is embedded in a building ventilation system, which is connected to various IEQ measurement sensors, it will offer a customized building ventilation system to the building occupants.

Original languageEnglish
Pages (from-to)46-59
Number of pages14
JournalBuilding and Environment
Volume153
DOIs
Publication statusPublished - 2019 Apr 15

Fingerprint

environmental quality
Ventilation
ventilation
Logistics
logistics
regression
building
environmental standards
performance
recipient
compliance
sensor
evaluation
indoor environment
Sensors

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Building and Construction

Cite this

@article{e6579871a7044558ba92d585294d4d76,
title = "Automatic ventilation control algorithm considering the indoor environmental quality factors and occupant ventilation behavior using a logistic regression model",
abstract = "Building occupants find it difficult to achieve the optimal indoor environment quality (IEQ) through natural ventilation. To solve this problem, this study aimed to develop an automatic ventilation control algorithm considering the IEQ factors and occupant ventilation behavior. The algorithm was developed in four steps: (i) real-time collection of data on the IEQ factors and occupant ventilation behavior; (ii) development of the automatic ventilation control algorithm using logistic regression; (iii) determination of the automatic ventilation control algorithm using receiver operating characteristic curve analysis; and (iv) evaluation of the automatic ventilation control algorithm's performance according to the indoor environmental standards. Through this process, the logistic regression model with ridge regression (area under curve: 0.865), with the highest classification accuracy, was selected. Then Youden's index was used to define the decision criterion (i.e., optimal cutoff value) for the logistic regression model. As a result, the decision criterion for opening and closing the windows or doors was 0.533. When the developed algorithm was compared with the indoor environmental standards to analyze its performance, the compliance rate of the opening of the windows or doors based on the monitored data was 77.6{\%}, but it increased to 99{\%} based on the data classified by the developed algorithm. It is expected that if the automatic ventilation control algorithm is embedded in a building ventilation system, which is connected to various IEQ measurement sensors, it will offer a customized building ventilation system to the building occupants.",
author = "Hakpyeong Kim and Taehoon Hong and Jimin Kim",
year = "2019",
month = "4",
day = "15",
doi = "10.1016/j.buildenv.2019.02.032",
language = "English",
volume = "153",
pages = "46--59",
journal = "Building and Environment",
issn = "0360-1323",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Automatic ventilation control algorithm considering the indoor environmental quality factors and occupant ventilation behavior using a logistic regression model

AU - Kim, Hakpyeong

AU - Hong, Taehoon

AU - Kim, Jimin

PY - 2019/4/15

Y1 - 2019/4/15

N2 - Building occupants find it difficult to achieve the optimal indoor environment quality (IEQ) through natural ventilation. To solve this problem, this study aimed to develop an automatic ventilation control algorithm considering the IEQ factors and occupant ventilation behavior. The algorithm was developed in four steps: (i) real-time collection of data on the IEQ factors and occupant ventilation behavior; (ii) development of the automatic ventilation control algorithm using logistic regression; (iii) determination of the automatic ventilation control algorithm using receiver operating characteristic curve analysis; and (iv) evaluation of the automatic ventilation control algorithm's performance according to the indoor environmental standards. Through this process, the logistic regression model with ridge regression (area under curve: 0.865), with the highest classification accuracy, was selected. Then Youden's index was used to define the decision criterion (i.e., optimal cutoff value) for the logistic regression model. As a result, the decision criterion for opening and closing the windows or doors was 0.533. When the developed algorithm was compared with the indoor environmental standards to analyze its performance, the compliance rate of the opening of the windows or doors based on the monitored data was 77.6%, but it increased to 99% based on the data classified by the developed algorithm. It is expected that if the automatic ventilation control algorithm is embedded in a building ventilation system, which is connected to various IEQ measurement sensors, it will offer a customized building ventilation system to the building occupants.

AB - Building occupants find it difficult to achieve the optimal indoor environment quality (IEQ) through natural ventilation. To solve this problem, this study aimed to develop an automatic ventilation control algorithm considering the IEQ factors and occupant ventilation behavior. The algorithm was developed in four steps: (i) real-time collection of data on the IEQ factors and occupant ventilation behavior; (ii) development of the automatic ventilation control algorithm using logistic regression; (iii) determination of the automatic ventilation control algorithm using receiver operating characteristic curve analysis; and (iv) evaluation of the automatic ventilation control algorithm's performance according to the indoor environmental standards. Through this process, the logistic regression model with ridge regression (area under curve: 0.865), with the highest classification accuracy, was selected. Then Youden's index was used to define the decision criterion (i.e., optimal cutoff value) for the logistic regression model. As a result, the decision criterion for opening and closing the windows or doors was 0.533. When the developed algorithm was compared with the indoor environmental standards to analyze its performance, the compliance rate of the opening of the windows or doors based on the monitored data was 77.6%, but it increased to 99% based on the data classified by the developed algorithm. It is expected that if the automatic ventilation control algorithm is embedded in a building ventilation system, which is connected to various IEQ measurement sensors, it will offer a customized building ventilation system to the building occupants.

UR - http://www.scopus.com/inward/record.url?scp=85062211133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062211133&partnerID=8YFLogxK

U2 - 10.1016/j.buildenv.2019.02.032

DO - 10.1016/j.buildenv.2019.02.032

M3 - Article

AN - SCOPUS:85062211133

VL - 153

SP - 46

EP - 59

JO - Building and Environment

JF - Building and Environment

SN - 0360-1323

ER -