TY - GEN
T1 - Advanced indoor thermal environment control using occupant's mean facial skin temperature and clothing level
AU - Li, Xuan
AU - Deng, Zhipeng
AU - Shi, Zhu
AU - Chen, Qingyan
N1 - Funding Information:
The data acquisition process in this study was reviewed by the Institutional Review Board at Purdue University with protocol #1811021298.
Publisher Copyright:
© 2020 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - People spend most time indoors, thus it is crucial to provide comfortable indoor thermal environment for occupants. This study developed an indoor thermal environment control strategy that can adjust thermostat setpoint accordingly and timely to optimize occupant's thermal comfort based on clothing level and mean facial skin temperature. The clothing level was measured by an RGB camera combined with a neural-network-based program. The mean facial skin temperature was measured by a long wave infrared (LWIR) camera combined with a face detection program. The clothing level measured was used to determine the temperature setpoint in an office. According to the mean facial skin temperature measured, the setpoint could be further adjusted. This investigation has validated this control strategy and found that 91% of the subjects tested felt neutral in the office.
AB - People spend most time indoors, thus it is crucial to provide comfortable indoor thermal environment for occupants. This study developed an indoor thermal environment control strategy that can adjust thermostat setpoint accordingly and timely to optimize occupant's thermal comfort based on clothing level and mean facial skin temperature. The clothing level was measured by an RGB camera combined with a neural-network-based program. The mean facial skin temperature was measured by a long wave infrared (LWIR) camera combined with a face detection program. The clothing level measured was used to determine the temperature setpoint in an office. According to the mean facial skin temperature measured, the setpoint could be further adjusted. This investigation has validated this control strategy and found that 91% of the subjects tested felt neutral in the office.
KW - Convolutional neural network
KW - Thermal comfort
KW - Thermostat setpoint control
UR - http://www.scopus.com/inward/record.url?scp=85101629961&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85101629961
T3 - 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020
BT - 16th Conference of the International Society of Indoor Air Quality and Climate
PB - International Society of Indoor Air Quality and Climate
T2 - 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020
Y2 - 1 November 2020
ER -