Development of a novel method to detect clothing level and facial skin temperature for controlling HVAC systems

Xuan Li, Qingyan Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


People spend most of their time indoors, and thus it is important to provide occupants with a comfortable indoor thermal environment. However, inappropriate thermostat temperature settings in offices make occupants less comfortable. This study developed a new control strategy for HVAC systems that adjusts the thermostat setpoint according to clothing level and mean facial skin temperature. An image-classification model was trained on the basis of a convolutional neural network (CNN) to classify the clothing level of occupants, which was then used to calculate a comfortable air temperature. This investigation used a long-wave infrared (LWIR) camera with a face-detection program to obtain occupants’ mean facial skin temperature. This study performed experimental tests to correlate mean facial skin temperature with thermal sensation votes. The mean facial skin temperature was then used to develop a control strategy for an HVAC system in a single-occupant office. With the use of the control strategy, 91% of the subjects tested in this investigation felt thermally neutral in the office.

Original languageEnglish
Article number110859
JournalEnergy and Buildings
Publication statusPublished - 15 May 2021


  • Clothing level
  • Image classification
  • Infrared thermography
  • Skin temperature
  • Thermal comfort
  • Thermostat setpoint

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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