Operation of typical heating, ventilation, and air conditioning (HVAC) systems has been based on general thermal comfort models. However, because of individual differences in thermal comfort, typical HVAC systems have not been able to achieve high levels of occupants' satisfaction. Moreover, conservative control settings designed for "widely acceptable" conditions have resulted in a high chance of energy waste.To resolve these issues, HVAC systems have been proposed that can (i) learn their

more »

... pants' thermal preferences; (ii) be self-tuned to provide customized indoor thermal environments. This Thesis presents approaches and algorithms for the realization of self-tuned indoor thermal environments and demonstrates their experimental and simulation implementations.First, this Thesis presents a new data-driven method for learning individual occupants' thermal preferences developed by combining classification and inference problems, without developing different models for each occupant. The approach is fully Bayesian, and it is based on the premise that the thermal preference is mainly governed by (i) an overall thermal stress, represented using physical process equations with relatively few parameters along with prior knowledge of the parameters, and (ii) the personal thermal preference characteristic, which is modeled as a hidden random variable. The concept of clustering occupants based on this hidden variable, i.e., similar thermal preference characteristic, is introduced. The algorithm also incorporates hidden parameters and informative priors to account for the uncertainty associated with variables that are noisy or difficult to measure (unobserved) in real buildings (for example, the metabolic rate, air speed and occupants' clothing level). Experimental results show that the algorithm provides accurate predictions for personalized thermal preference profiles and it is efficient as it only requires a relatively small dataset collected from each occupant.Second, this Thesis presents a self-tuned HVA [...]