Prediction of occupants perception of natural ventilation effectiveness

Le Li; Nima Izadyar; Tim Law; Keivan Bamdad

Prediction of occupants perception of natural ventilation effectiveness

Le Li, Nima Izadyar, Tim Law, Keivan Bamdad

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

![CDATA[Natural ventilation (NV) is an effective strategy to reduce building energy use and improve the indoor air quality. Prediction of occupants’ perception of NV effectiveness can provide insights into better design of NV strategies in buildings. Accordingly, in this paper, firstly, data from influential variables on NV including balcony and room features along with demographic characteristics in 195 number of apartments was collected. Two datasets were then developed to represent apartments with single-sided and double-sided natural ventilation. Finally, a fuzzy neural network (FNN) developed to predict occupants' perception of the NV effectiveness. Results showed that FNN model can predict occupants' perceptions with over 90 per cent accuracy in our case studies.]]

Original language	English
Title of host publication	Proceedings of the 2022 Australasian Building Simulation Conference, Brisbane, Australia, 20-21 July 2022
Publisher	Australian Institute of Refrigeration, Air-conditioning and Heating
Pages	231-241
Number of pages	11
ISBN (Print)	9780949436542
Publication status	Published - 2022
Event	Australasian Building Simulation Conference - Duration: 20 Jul 2022 → …

Conference

Conference	Australasian Building Simulation Conference
Period	20/07/22 → …

Cite this

@inproceedings{254c42878b20469baea9991a9df211c9,

title = "Prediction of occupants perception of natural ventilation effectiveness",

abstract = "![CDATA[Natural ventilation (NV) is an effective strategy to reduce building energy use and improve the indoor air quality. Prediction of occupants{\textquoteright} perception of NV effectiveness can provide insights into better design of NV strategies in buildings. Accordingly, in this paper, firstly, data from influential variables on NV including balcony and room features along with demographic characteristics in 195 number of apartments was collected. Two datasets were then developed to represent apartments with single-sided and double-sided natural ventilation. Finally, a fuzzy neural network (FNN) developed to predict occupants' perception of the NV effectiveness. Results showed that FNN model can predict occupants' perceptions with over 90 per cent accuracy in our case studies.]]",

author = "Le Li and Nima Izadyar and Tim Law and Keivan Bamdad",

year = "2022",

language = "English",

isbn = "9780949436542",

pages = "231--241",

booktitle = "Proceedings of the 2022 Australasian Building Simulation Conference, Brisbane, Australia, 20-21 July 2022",

publisher = "Australian Institute of Refrigeration, Air-conditioning and Heating",

note = "Australasian Building Simulation Conference ; Conference date: 20-07-2022",

}

Prediction of occupants perception of natural ventilation effectiveness. / Li, Le; Izadyar, Nima; Law, Tim et al.
Proceedings of the 2022 Australasian Building Simulation Conference, Brisbane, Australia, 20-21 July 2022. Australian Institute of Refrigeration, Air-conditioning and Heating, 2022. p. 231-241.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Prediction of occupants perception of natural ventilation effectiveness

AU - Li, Le

AU - Izadyar, Nima

AU - Law, Tim

AU - Bamdad, Keivan

PY - 2022

Y1 - 2022

N2 - ![CDATA[Natural ventilation (NV) is an effective strategy to reduce building energy use and improve the indoor air quality. Prediction of occupants’ perception of NV effectiveness can provide insights into better design of NV strategies in buildings. Accordingly, in this paper, firstly, data from influential variables on NV including balcony and room features along with demographic characteristics in 195 number of apartments was collected. Two datasets were then developed to represent apartments with single-sided and double-sided natural ventilation. Finally, a fuzzy neural network (FNN) developed to predict occupants' perception of the NV effectiveness. Results showed that FNN model can predict occupants' perceptions with over 90 per cent accuracy in our case studies.]]

AB - ![CDATA[Natural ventilation (NV) is an effective strategy to reduce building energy use and improve the indoor air quality. Prediction of occupants’ perception of NV effectiveness can provide insights into better design of NV strategies in buildings. Accordingly, in this paper, firstly, data from influential variables on NV including balcony and room features along with demographic characteristics in 195 number of apartments was collected. Two datasets were then developed to represent apartments with single-sided and double-sided natural ventilation. Finally, a fuzzy neural network (FNN) developed to predict occupants' perception of the NV effectiveness. Results showed that FNN model can predict occupants' perceptions with over 90 per cent accuracy in our case studies.]]

UR - https://hdl.handle.net/1959.7/uws:67832

M3 - Conference Paper

SN - 9780949436542

SP - 231

EP - 241

BT - Proceedings of the 2022 Australasian Building Simulation Conference, Brisbane, Australia, 20-21 July 2022

PB - Australian Institute of Refrigeration, Air-conditioning and Heating

T2 - Australasian Building Simulation Conference

Y2 - 20 July 2022

ER -

Prediction of occupants perception of natural ventilation effectiveness

Abstract

Conference

Other files and links

Fingerprint

Cite this