Advances in Information and Communication Technology (ICT) paved the way for decentralized Heating, Ventilation, and Air-Conditioning (HVAC) HVAC operations. It has been envisioned that development of personal thermal comfort profiles leads to accurate predictions of each occupant's thermal comfort state and such information is employed in context-aware HVAC operations for energy efficiency. This dissertation has three key contributions in realizing this envisioned HVAC operation. First, it presents a systematic review of research trends and developments in context-aware HVAC operations. Second, it contributes to expanding the feasibility of the envisioned HVAC operation by introducing novel sensing technologies. Third, it contributes to shedding light on viability and potentials of comfort-aware operations (i.e., integrating personal thermal comfort models into HVAC control logic) through a comprehensive assessment of energy efficiency implications. In the first contribution, by developing a taxonomy, two major modalities – occupancy-driven and comfort-aware operations – in Human-In-The-Loop (HITL) HVAC operations were identified and reviewed quantitatively and qualitatively. The synthesis of previous studies has indicated that field evaluations of occupancy-driven operations showed lower potentials in energy saving, compared to the ones with comfort-aware operations. However, the results in comfort-aware operations could be biased given the small number of explorations. Moreover, required data representation schema have been presented to foster constructive performance assessments across different research efforts. In the end, the current state of research and future directions of HITL HVAC operations were discussed to shed light on future research need. As the second contribution, moving toward expanding the feasibility of comfort-aware operations, novel and smart sensing solutions have been introduced. It has been noted that, in order to have high accuracy in predicting individual's thermal comfort state (≥90%), user physiological response data play a key part. However, the limited number of applicable sensing technologies (e.g., infrared cameras) has impeded the potentials of implementation. After defining required characteristics in physiological sensing solutions in context of comfort-aware operations (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, Doppler radar sensors, and heat flux sensors were evaluated. RGB cameras, available in many smart computing devices, could be a ubiquitous solution in quantifying thermoregulation states. Leveraging the mechanism of skin blood perfusion, two thermoregulation state quantification methods have been developed. Then, applicability and sensitivity were checked with two experimental studies. In the first experimental study aiming to see applicability (distinguishing between 20 and 30C with fully acclimated human bodies), for 16 out of 18 human subjects, an increase in their blood perfusion was observed. In the second experimental study aiming to evaluate sensitivity (distinguishing responses to a continuous variation of air temperature from 20 to 30C), 10 out of 15 subjects showed a positive correlation between blood perfusion and thermal sensations. Also, the superiority of heat flux data, compared to skin temperature data, has been demonstrated in predicting personal thermal comfort states through the developments of machine-learning-based prediction models with feature engineering. Specifically, with random forest classifier, the median value of prediction accuracy was improved by 3.8%. Lastly, Doppler radar sensors were evaluated for their capability of quantifying user thermoregulation states leveraging the periodic movement of the chest/abdomen area induced by respiration. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). On the other hand, in a transient temperature without acclimation time, it was shown that, some of the human subjects (38.9%) used respiration as an active means of heat exchange for thermoregulation. Lastly, a comprehensive evaluation of comfort-aware operations' performance was carried out with a diverse set of contextual and operational factors. First, a novel comfort-aware operation strategy was introduced to leverage personal sensitivity to thermal comfort (i.e., different responses to temperature changes; e.g., sensitive to being cold) in optimization. By developing an agent-based simulation framework and thorough diverse scenarios with different numbers and combinations of occupants (i.e., human agents in the simulation), it was shown that this approach is superior in generating collectively satisfying environments against other approaches focusing on individual preferred temperatures in selection of optimized setpoints. The energy implications of comfort-aware operations were also evaluated to understand the impact from a wide range of factors (e.g., human and building factors) and their combinatorial effect given the uncertainty of multioccupancy scenarios. The results demonstrated that characteristics of occupants' thermal comfort profiles are dominant in impacting the energy use patterns, followed by the number of occupants, and the operational strategies. In addition, when it comes to energy efficiency, more occupants in a thermal zone/building result in reducing the efficacy of comfort-driven operation (i.e., the integration of personal thermal comfort profiles). Hence, this study provided a better understanding of true viability of comfort-driven HVAC operations and provided the probabilistic bounds of energy saving potentials. These series of studies have been presented as seven journal articles and they are included in this dissertation. / Doctor of Philosophy / With vision of a smart built environment, capable of understanding the contextual dynamics of built environment and adaptively adjusting its operation, this dissertation contributes to context-aware/decentralized HVAC operations. Three key contributions in realization of this goal include: (1) a systematic review of research trends and developments in the last decade, (2) enhancing the feasibility of quantifying personal thermal comfort by presenting novel sensing solutions, and (3) a comprehensive assessment of energy efficiency implications from comfort-aware HVAC operations with the use of personal comfort models. Starting from identifying two major modalities of context-aware HVAC operations, occupancy-driven and comfort-aware, the first part of this dissertation presents a quantitative and qualitative review and synthesis of the developments, trends, and remaining research questions in each modality. Field evaluation studies using occupancy-driven operations have shown median energy savings between 6% and 15% depending on the control approach. On the other hand, the comfort-aware HVAC operations have shown 20% energy savings, which were mainly derived from small-scale test beds in similar climate regions. From a qualitative technology development standpoint, the maturity of occupancy-driven technologies for field deployment could be interpreted to be higher than comfort-aware technologies while the latter has shown higher potentials. Moreover, by learning from the need for comparing different methods of operations, required data schemas have been proposed to foster better benchmarking and effective performance assessment across studies. The second part of this dissertation contributes to the cornerstone of comfort-aware operations by introducing novel physiological sensing solutions. Previous studies demonstrated that, in predicting individual's thermal comfort states, using physiological data in model development plays a key role in increasing accuracy (>90%). However, available sensing technologies in this context have been limited. Hence, after identifying essential characteristics for sensing solutions (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, heat flux sensors, and Doppler radar sensors were evaluated. RGB cameras, available in many smart devices, could be programmed to measure the level of blood flow to skin, regulated by the human thermoregulation mechanism. Accordingly, two thermoregulation states' quantification methods by using RGB video images have been developed and assessed under two experimental studies: (i) capturing subjects' facial videos in two opposite temperatures with sufficient acclimation time (20 and 30C), and (ii) capturing facial videos when subjects changed their thermal sensations in a continuous variation of air temperature from 20 to 30C. Promising results were observed in both situations. The first study had subjects and 16 of them showed an increasing trend in blood flow to skin. In the second study, posing more challenges due to insufficient acclimation time, 10 subjects had a positive correlation between the level of blood flow to skin with thermal sensation. With the assumption that heat flux sensing will be a better reflection of thermoregulation sates, a machine learning framework was developed and tested. The use of heat flux sensing showed an accuracy of 97% with an almost 4% improvement compared to skin temperature. Lastly, Doppler radar sensors were evaluated for their capability of quantifying thermoregulation states by detecting changes in breathing patterns. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). However, using a transient temperature was proven to be more challenging. It was noted that for some of the human subjects (38.9%), respiration was detected as an active means of heat exchange. It was concluded that specialized artifact removal algorithms might help improve the detection rate. The third component of the dissertation contributed by studying the performance of comfort-driven operations (i.e., using personal comfort preferences for HVAC operations) under a diverse set of contextual and operational factors. Diverse scenarios for interaction between occupants and building systems were evaluated by using different numbers and combinations of occupants, and it was demonstrated that an approach of addressing individual's thermal comfort sensitivity (personal thermal-comfort-related responses to temperature changes) outperforms other approaches solely focusing on individual preferred temperatures. The energy efficiency implications of comfort-driven operations were then evaluated by accounting for the impact of human and building factors (e.g., number of thermal zones) and their combinations. The results showed that characteristics of occupants' thermal comfort profiles are dominant in driving the energy use patterns, followed by the number of occupants, and operational strategies. As one of the main outcomes of this study, the energy saving and efficiency (energy use for comfort improvement) potentials and probabilistic bounds of comfort-driven operations were identified. It was shown that keeping the number of occupants low (under 6) in a thermal zone/building, boosts the energy saving potentials of comfort-driven operations. These series of studies have been presented as seven journal articles, included in this dissertation.

<p dir="ltr">Personalized thermal controls are beneficial for occupant comfort and productivity in office buildings. Recent research efforts on learning personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This Thesis presents the development and field implementation of personal preference-based thermal control in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles, a low-cost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. The customized, preference-based HVAC control implemented in the offices indicated the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28-35% energy savings with personalized MPC (vs personalized static setpoint control).</p><p dir="ltr">Regarding the practical limitations in collecting sufficient data from occupants to train their thermal comfort model, we present a Bayesian meta-learning approach for developing reliable, data-driven personalized thermal comfort models using limited data from individuals. A high-dimensional neural network was developed, considering general thermal comfort impact factors (environmental variables, clothing level and metabolic rate) as well as personal thermal characteristics (expressed as a vector of continuous latent variables) as model inputs. The model parameters in the neural network were trained with subsets of ASHRAE RP-884 database. The trained neural network is transferrable, so that the thermal preferences of new individuals can be predicted by inferring their personal thermal characteristics using limited data. The results show that the developed Bayesian meta-learning approach to infer personal thermal comfort performs better than existing methods, especially when using limited data.</p><p dir="ltr">Moreover, this Thesis also discusses the potential of balancing thermal comfort and energy cost by setting dynamic temperature constraints in personalized MPC. A co-simulation framework of EnergyPlus and MPC is constructed using EnergyPlus Python API. Dynamic temperature constraints are selected based on personal thermal profile, weather conditions and utility rate variations. The performance of the personalized MPC with dynamic constraints demonstrates a balance between thermal comfort and energy cost in cooling season.</p>