Hot, cold, or just right? An infrared biometric sensor to improve occupant comfort and reduce overcooling in buildings via closed-loop control

• Published in: Energy and buildings Vol. 312; p. 114063
• Main Authors: Levinson, Ronnen, Kim, Donghun, Goudey, Howdy, Chen, Sharon, Zhang, Hui, Ghahramani, Ali, Huizenga, Charlie, He, Yingdong, Nomoto, Akihisa, Arens, Edward, Álvarez Suárez, Ana, Ritter, David, Tarin, Markus, Prickett, Robert
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.06.2024; Elsevier
• Subjects: Air conditioning; Closed-loop control; ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Energy savings; Infrared thermography; Machine vision; Overcooling; Skin temperature; Thermal comfort; Air conditioning; Infrared thermography; Overcooling; Thermal comfort; Machine vision; Skin temperature; Energy savings; Closed-loop control
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2024.114063

[Display omitted] •Thermostatic air-conditioning control can discomfort occupants by overcooling spaces.•Machine vision + infrared thermography measures face, nose, and hand temperatures.•Human skin temperature distribution predicts cool/neutral/warm thermal sensation.•Infrared biometric sensor-controller targets neutral-to-slightly warm sensation.•Office-building trial demonstrated comfort improvement and 42% cooling-load reduction. To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.