Development and validation of a smart HVAC control system for multi-occupant offices by using occupants’ physiological signals from wristband

• Published in: Energy and buildings Vol. 214; p. 109872
• Main Authors: Deng, Zhipeng, Chen, Qingyan
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 01-05-2020; Elsevier BV
• Subjects: Air temperature; Artificial neural network; Bluetooth; Computer simulation; Control systems; Cooling loads; Cooling systems; Energy conservation; Energy consumption; Heart rate; Humidity; HVAC; HVAC equipment; Indoor environments; Mathematical models; Occupancy; Offices; Parameters; Physiology; Relative humidity; Skin; Skin relative humidity; Skin temperature; Thermal comfort; Thermostat set point; Wearable device; Wrist; Heart rate; Thermal comfort; Wearable device; Skin relative humidity; Air temperature; Skin temperature; Thermostat set point; Artificial neural network
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2020.109872

•Accurately predict TSV in offices in real time with physiological signals from wristband.•Develop and validate a smart HVAC control system in multi-occupant offices by using physiological and indoor environmental parameters.•Improve the overall thermal comfort in multi-occupant offices by using the wristband control system.•Energy use of the developed wristband control system was almost the same as using constant set point.
Since people spend most of their time indoors, it is important to create comfortable indoor environments for building occupants. However, unsuitable thermostat settings lead to energy waste and an undesirable indoor environment, especially in multi-occupant rooms. This study aimed to develop and validate a control strategy for the HVAC systems in multi-occupant offices using physiological parameters measured by wristbands. We used an ANN model to predict thermal sensation from indoor environmental and physiological parameters such as air temperature, relative humidity, clothing level, wrist skin temperature, skin relative humidity and heart rate. The model was trained by data collected in seven multi-occupant offices in the course of a year, and it was able to predict the thermal sensation with high accuracy. Next, we developed a control strategy for the HVAC system to improve the thermal comfort of all the occupants in the room. The control system was smart and could adjust the thermostat set point automatically in real time. We validated the system by means of both experiments and numerical simulations. In most cases, we improved the occupants’ thermal comfort level. After using the wristband control, over half of the occupants experienced a neutral sensation, and fewer than 5% still felt uncomfortable. The energy consumption by the HVAC system with the wristband control was almost the same as when the constant set point was used. After coupling with occupancy-based control by means of lighting sensors or wristband Bluetooth, the heating and cooling loads were reduced by 90% and 30%, respectively, in interior offices. Therefore, the smart HVAC control system can effectively control the indoor environment for thermal comfort and energy saving.