TY - GEN
T1 - Non-Intrusive Method for Capturing Occupant Thermal Discomfort Cues and Profiles in Buildings
AU - Bucarelli, Nidia
AU - El-Gohary, Nora
N1 - Publisher Copyright:
© 2022 Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics - Selected Papers from Construction Research Congress 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Most building energy-saving strategies are based on thermal comfort standards defined by regulatory bodies (e.g., ASHRAE), which do not account for individual differences or preferences of the building occupants. To address this gap, this paper proposes a non-intrusive automated method to capture the individual thermal discomfort states of occupants over time based on their thermal discomfort cues. The method aims to support the identification of personalized energy-saving strategies toward improved occupant thermal comfort and reduced building energy consumption. It uses a deep learning model to recognize occupant cues from videos. To implement and test the proposed approach, participants were video recorded in uncontrolled office settings while performing work-related activities. The deep learning model was used to recognize the thermal discomfort cues, and a thermal comfort profile that captures the discomfort states of occupants over time was developed. The method achieved a weighted recall and precision of 84% and 96%, respectively, in recognizing building occupant thermal discomfort cues.
AB - Most building energy-saving strategies are based on thermal comfort standards defined by regulatory bodies (e.g., ASHRAE), which do not account for individual differences or preferences of the building occupants. To address this gap, this paper proposes a non-intrusive automated method to capture the individual thermal discomfort states of occupants over time based on their thermal discomfort cues. The method aims to support the identification of personalized energy-saving strategies toward improved occupant thermal comfort and reduced building energy consumption. It uses a deep learning model to recognize occupant cues from videos. To implement and test the proposed approach, participants were video recorded in uncontrolled office settings while performing work-related activities. The deep learning model was used to recognize the thermal discomfort cues, and a thermal comfort profile that captures the discomfort states of occupants over time was developed. The method achieved a weighted recall and precision of 84% and 96%, respectively, in recognizing building occupant thermal discomfort cues.
UR - http://www.scopus.com/inward/record.url?scp=85128889785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128889785&partnerID=8YFLogxK
U2 - 10.1061/9780784483961.020
DO - 10.1061/9780784483961.020
M3 - Conference contribution
AN - SCOPUS:85128889785
T3 - Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics - Selected Papers from Construction Research Congress 2022
SP - 185
EP - 194
BT - Construction Research Congress 2022
A2 - Jazizadeh, Farrokh
A2 - Shealy, Tripp
A2 - Garvin, Michael J.
PB - American Society of Civil Engineers
T2 - Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics, CRC 2022
Y2 - 9 March 2022 through 12 March 2022
ER -