Towards Real-Time Model-Based Monitoring and Adoptive Controlling of Indoor Thermal Comfort

Thermal comfort is an important aspect of the building design and indoor climate control as modern man spends most of the day indoors. Conventional indoor climate design and control approaches are based on static thermal comfort models that views the building occupants as passive recipients of their thermal environment. Assuming that people have relatively constant range of biological comfort requirements, and that the indoor environmental variables should be controlled to conform to that constant range. The (r)evolution in modern sensing and computing technologies (price, compact size, flexibility and stretchability) is making it possible to continuously measure signals in real-time from human body using wearable technologies and smart clothing. Many advanced and accurate mechanistic thermoregulation models, such as the ‘Fiala thermal Physiology and Comfort’ model, are developed to assess the thermal strains and comfort status of humans. However, the most reliable mechanistic models are too complex to be implemented in real-time for monitoring and control applications. Additionally, such models are using not-easily or invasively measured variables (e.g., core temperatures), which are often not practical and undesirable measurements for monitoring during varied activities over prolonged periods. The purpose of this work is to develop a databased mechanistic (grey box) model, with minimum number of parameters and non-invasive input variables, for real-time monitoring and controlling of individual occupant’s thermal comfort.