Abstract
In recent years, physiological features have gained more attention in developing models of personal thermal comfort for improved and accurate adaptive operation of Human-In-The-Loop (HITL) Heating, Ventilation, and Air-Conditioning (HVAC) systems. Pursuing the identification of effective physiological sensing systems for enhancing flexibility of human-centered and distributed control, using machine learning algorithms, we have investigated how heat flux sensing could improve personal thermal comfort inference under transient ambient conditions. We have explored the variations of heat exchange rates of facial and wrist skin. These areas are often exposed in indoor environments and contribute to the thermoregulation mechanism through skin heat exchange, which we have coupled with variations of skin and ambient temperatures for inference of personal thermal preferences. Adopting an experimental and data analysis methodology, we have evaluated the modeling of personal thermal preference of 18 human subjects for well-known classifiers using different scenarios of learning. The experimental measurements have revealed the differences in personal thermal preferences and how they are reflected in physiological variables. Further, we have shown that heat exchange rates have high potential in improving the performance of personal inference models even compared to the use of skin temperature.
Highlights
In the design and operation of HVAC systems, collective models, most notably the Predicted Mean Vote (PMV) model, have been used to identify the configuration of the building systems according to the occupant comfort and to reflect occupants’ perspectives
The PMV model presumes that the neutral state on the ASHARE thermal sensation scale is the preferred state by occupants [4]
By measuring the variations of most commonly used thermophysiological attribute, skin temperature, in parallel with heat exchange rates we have explored the relationship between human thermoregulation mechanism and human-environment heat exchange
Summary
Human-centered operation of Heating, Ventilation, and Air-Conditioning (HVAC) systems has gained attention in the last decade given its potentials for improved energy efficiency by enhancing occupant comfort and reducing energy consumption [1,2,3]. Electronic mediums (e.g., web-based [11] and smartphone-based [15]) have been proposed to facilitate data collection processes and improve the flow of information from occupants to building systems, enabling occupants to provide their feedback for the control of HVAC systems These efforts further have proceeded with compiling data on personal thermal feedback (using different thermal sensation scales) in concomitant with measuring the environmental ambient and contextual conditions (such as temperature, humidity, clothing level, and seasonal information) in the form of comfort datasets [3,16,17,18]. The infrared imaging technology has drawn attention due to its non-intrusive nature [23,26,27], which is one of the important factors in feasibility of human-building interaction applications [28,29,30,31]
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