Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.

The growing environmental concerns and the significant energy consumption in hotel buildings make the ability to proactively manage energy and lower carbon intensity essential in the global hospitality industry. Activating guests’ energy-conservation behaviours is a potential strategy for sustainable hotel operation and maintenance. Yet, the psychological mechanism of hotel building energy-conservation intention and the roles of personality traits have not been sufficiently investigated. This study aims to examine the role of guests’ extraversion levels in their hotel building energy-conservation behavioural intention using a modified theory of planned behaviour (TPB) model. The study extends the TPB model with personal norms and past behaviour as two additional factors and employs past behaviour as a moderator to bridge extraversion and other psychological factors. A field experiment was conducted consisting of 530 hotel guests in Shanghai, China. The results demonstrate the relationships between attitude, behavioural control, personal norms, past behaviour and energy-conservation intention. Specifically, extraversion negatively influences perceived behavioural control (PBC) (β = −0.176, p < 0.001) and positively impacts on personal norms (β = 0.290, p < 0.001), both of which significantly contribute to guest energy-saving intention. In addition, past behaviours positively moderate the effects of extraversion on subjective norms and personal norms. This research enriches the hospitality and tourism management literature by shedding novel light on how guests’ personality characteristics influence their pro-environment intentions during their stays in hotel buildings. The findings would drive the hotel building energy management forward through actionable and effective energy-conservation interventions and enhanced guest satisfaction.

As humans spend more time indoors, ensuring acceptable indoor air quality (IAQ) through ubiquitous sensing systems has become a necessity. Although extensive studies have been conducted on the IAQ sensing systems, a holistic review of the performance and deployment of Ubiquitous IAQ Sensing (UIAQS) systems with associated requirements in IAQ sensing standards is still lacking. In this study, we first reviewed IAQ pollutants and other IAQ-related factors and the associated requirements in the prominent IAQ sensing standards. We found that while non-pollutant factors are influential on occupants’ perception of IAQ and their satisfaction, they do not have evaluation metrics in the IAQ standards. Then, we systematically reviewed field studies on UIAQS technologies in the literature. Specific classes of information were recorded and analyzed further. We found that the majority of the UIAQS systems did not meet the requirements of the prominent IAQ sensing standards and identified four primary research gaps. We concluded that a new holistic and personalized approach that incorporates UIAQS measurements and subjective feedback is needed. This study provides valuable insights for researchers and policymakers to better improve UIAQS technologies by developing personalized IAQ sensors and sensing standards.