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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.

To identify and characterize localized urban heat- and cool-island signals embedded within the temperature field of a large urban-climate archipelago, fine-resolution simulations with a modified urbanized version of the WRF meteorological model were carried out as basis for siting fixed weather monitors and designing mobile-observation transects. The goal was to characterize variations in urban heat during summer in Los Angeles, California. Air temperatures measured with a shielded sensor mounted atop an automobile in the summers of 2016 and 2017 were compared to model output and also correlated to surface physical properties focusing on neighborhood-scale albedo and vegetation canopy cover. The study modeled and measured the temperature response to variations in surface properties that already exist in the real world, i.e., realistic variations in albedo and canopy cover that are attainable through current building and urban design practices. The simulated along-transect temperature from a modified urbanized WRF model was compared to the along-transect observed temperature from 15 mobile traverses in one area near downtown Los Angeles and another in an inland basin (San Fernando Valley). The observed transect temperature was also correlated to surface physical properties characterizations that were developed for input to the model. Both comparisons were favorable, suggesting that (1) the model can reliably be used in siting fixed weather stations and designing mobile-transect routes to characterize urban heat and (2) that except for a few cases with opposite co-varying influences, the correlations between observed temperature and albedo and between observed temperature and canopy cover were each negative, ranging from &minus, 1.0 to &minus, 9.0 &deg, C per 0.1 increase in albedo and from &minus, 0.1 to &minus, 2.2 &deg, C per 0.1 increase in canopy cover. Observational data from the analysis domains pointed to a wind speed threshold of 3 m/s. Below this threshold the variations in air temperature could be explained by land use and surface properties within a 500-m radius of each observation point. Above the threshold, air temperature was influenced by the properties of the surface within a 1-km upwind fetch. Of relevance to policy recommendations, the study demonstrates the significant real-world cooling effects of increasing urban albedo and vegetation canopy cover. Based on correlations between the observed temperature (from mobile transects) and surface physical properties in the study domains, the analysis shows that neighborhood-scale (500-m) cooling of up to 2.8 &deg, C during the daytime can be achieved by increasing albedo. A neighborhood can also be cooled by up to 2.3 &deg, C during the day and up to 3.3 &deg, C at night by increasing canopy cover. The analysis also demonstrates the suitability of using fine-resolution meteorological models to design mobile-transect routes or site-fixed weather monitors in order to quantify urban heat and the efficacy of albedo and canopy cover countermeasures. The results also show that the model is capable of accurately predicting the geographical locations and the magnitudes of localized urban heat and cool islands. Thus the model results can also be used to devise urban-heat mitigation measures.