HEAT - Human Embodied Autonomous Thermostat. (July 2020)

• Record Type: Journal Article
• Authors: Li, Da; Menassa, Carol C.; Kamat, Vineet R.; Byon, Eunshin
• Is Part Of: Building and environment. Volume 178(2020)
• Publication Date: 2020-07
• Subjects: Human occupants as thermostats -- Thermal comfort -- Personal comfort model -- Physiological sensing -- Physiological predictive model -- Temperature setpoint optimization; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2020.106879
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: The lack of thermal comfort among occupants is a common problem in built environments. Recent studies have investigated various physiological sensing and modeling approaches and demonstrated more robust thermal comfort prediction than the Predicted Mean Vote and participatory sensing methods. However, such physiological sensing approaches only work with iterative and passive Heating, Ventilation, and Air Conditioning (HVAC) control schemas which can lead to problems including uncertainties in setpoint control outcomes and interruptions to building occupants. To address this critical limitation, this paper proposes a new paradigm named Human Embodied Autonomous Thermostat (HEAT) that considers human occupants as an embodiment of smart and connected thermostats where physiological measurements in form of facial skin temperature can be used to directly communicate with and control HVAC operations for improved thermal satisfaction and reduced energy use while maintaining comfort in multi-occupancy spaces. This paradigm leverages occupants' skin temperature responses under different thermal environments and integrates two types of personal models - thermal comfort model and physiological predictive model to determine occupants' comfort, which can be represented as the thermal comfort zone and comfort probability. Based on these two metrics, three HVAC strategies are compared to demonstrate thermal comfort optimization for a group of occupants. The result suggests different setpoint options as a trade-off between overall comfort and energy use. The proposed HEAT framework can conceptually make wall-mounted physical thermostats redundant by serving as a basis for automated environment control based directly on human measurements to improve personalized human experience, well-being, and building energy efficiency. Highlights: Human physiological data allow for proactive thermal comfort control. A framework for setpoint optimization in multi-occupancy environments is proposed. Setpoint is determined by individuals' thermal comfort zone and comfort probability. Personal thermal comfort is predicted from facial skin temperature. Impacts of room temperature on individuals' skin temperature have been modeled.