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International building certification systems, such as the WELL and Leadership in Energy and Environmental Design (LEED) standards, play a pivotal role in the design of healthy and sustainable buildings. While LEED adopts a holistic approach to designing healthy and sustainable buildings, the WELL standard has a strong emphasis on human health, comfort, and well-being. Although prior research has revealed inconsistent results for occupant satisfaction in office buildings with WELL certification compared to buildings without WELL certification, or are certified using another certification system (e.g., LEED), most of these comparisons tend to lack methodological rigor. This study used a statistical procedure to match and compare 1634 occupant surveys from LEED-certified buildings to 1634 surveys from WELL-certified buildings. Six important architectural and experiential parameters were matched, masking their influence on the outcome. Overall building and workspace satisfaction was high in both WELL-certified buildings (94% and 87%) and LEED-certified (73% and 71%). We found that there is a 39% higher probability of finding occupants who are more satisfied in WELL-certified buildings compared to LEED-certified buildings, indicating occupant satisfaction is higher in buildings with WELL certification. Although we were unable to pinpoint the reason for higher occupant satisfaction in WELL-certified buildings, the results consistently showed that perceived indoor environmental quality was enhanced across all parameters except for the amount of space.

Causal thinking emphasizes the understanding of asymmetric causal relationships between variables, requiring us to specify which variable is the cause (independent variable) and which is the effect (dependent variable). Reversing the causal relationship direction can lead to profoundly different assumptions and interpretations. We demonstrate this by comparing two linear regression approaches used in thermal comfort research: Approach (a), which regresses thermal sensation votes (y-axis) on indoor temperature (x-axis); Approach (b), which does the reverse, regressing indoor temperature (y-axis) on thermal sensation votes (x-axis). From a correlational perspective, they may appear interchangeable, but causal thinking reveals substantial and practical differences between them. Approach (a) represents occupants’ thermal sensations as responses to indoor temperature. In contrast, Approach (b), rooted in adaptive comfort theory, suggests that thermal sensations can trigger behavioral changes, which in turn alter indoor temperature. Using the same data, we found that two approaches lead to different neutral temperatures and comfort zones. Approach (b) leads to what we call a ‘preferred zone’, which is 10 °C narrower than the conventionally derived comfort zone using Approach (a). We hypothesize that the ‘preferred zone’ might be interpreted as thermal conditions that occupants are likely to choose when they have significant control over their personal and environmental thermal settings. This finding has important implications for occupant comfort and building energy efficiency. We highlight the importance of integrating causal thinking into correlation-based statistical methods, which have been prevalent in building science research, especially given the increasing volume of data in the built environment.

Sleep is a pillar of human health and wellbeing. In high- and middle-income countries, there is a great reliance on heating, ventilation, and air conditioning systems (HVAC) to control the interior thermal environment in the bedroom. However, these systems are expensive to buy, maintain, and operate while being energy and environmentally intensive-problems that may increase due to climate change. Easily-accessible passive and low-energy strategies, such as fans and electrical heated blankets, address these challenges but their comparative effectiveness for providing comfort in sleep environments has not been studied. We used a thermal manikin to experimentally show that many passive and low-energy strategies are highly effective in supplementing or replacing HVAC systems during sleep. Using passive strategies in combination with low-energy strategies that elevate air movement like ceiling or pedestal fans enhances the cooling effect by three times compared to using fans alone. We extrapolated our experimental findings to estimate heating and cooling effects in two historical case studies: the 2015 Pakistan heat wave and the 2021 Texas power crisis. Passive and low-energy strategies reduced sleep-time heat or cold exposure by 69-91%. The low-energy strategies we tested require one to two orders of magnitude less energy than HVAC systems, and the passive strategies require no energy input. These strategies can also help reduce peak load surges and total energy demand in extreme temperature events. This reduces the need for utility load shedding, which can put individuals at risk of hazardous heat or cold exposure. Our results may serve as a starting point for evidence-based public health guidelines on how individuals can sleep better during heat waves and cold snaps without relying on HVAC.

Growth in energy use for indoor cooling tripled between 1990 and 2016 to outpace any other end use in buildings. Part of this energy demand is wasted on excessive cooling of offices, a practice known as overcooling. Overcooling has been attributed to poorly designed or managed air-conditioning systems with thermostats that are often set below recommended comfort temperatures. Prior research has reported lower thermal comfort for women in office buildings, but there is insufficient evidence to explain the reasons for this disparity. We use two large and independent datasets from US buildings to show that office temperatures are less comfortable for women largely due to overcooling. Survey responses show that uncomfortable temperatures are more likely to be cold than hot regardless of season. Crowdsourced data suggests that overcooling is a common problem in warm weather in offices across the US. The associated impacts of this pervasive overcooling on well-being and performance are borne predominantly by women. The problem is likely to increase in the future due to growing demand for cooling in increasingly extreme climates. There is a need to rethink the approach to air-conditioning office buildings in light of this gender inequity caused by overcooling.

Personal Comfort Systems (PCS) promise to reduce the energy needed to condition indoor environments, while also enhancing their occupants’ thermal pleasure. To explore these potentials in heating conditions, we compared the effectiveness of PCS heating various portions of the occupant against the normal Air Conditioning (AC) practice of warming the room volume. Twenty subjects experienced three modes of heating (AC only, AC together with PCS, and PCS only) at three initial room air temperatures (14, 16, and 18°C) and were given some control options throughout the testing. Skin temperatures, thermal pleasantness, and thermal sensation votes were recorded during the exposures. The PCS heating was more effective than AC control at alleviating occupant discomfort. With PCS present, the three initial room temperatures produced equivalent positive perceptions of thermal pleasantness and sensation. Providing occupants with AC control did not influence this result. AC alone did not produce appreciable alliesthesia due to its slow rate of changing the room temperature. In contrast, PCS produced an immediate pleasantness experience with its faster-acting conductive and radiative heating spread non-uniformly across the body. Whole-body thermal pleasantness closely followed the pleasantness of local body parts experiencing thermal stimuli. These temporal and spatial characteristics give PCS a significant advantage in generating thermal pleasure over traditional AC systems.

Studies often aim to determine which indoor environmental quality parameters best predict the overall workspace assessment. However, this method overlooks important differences distinguishing satisfied and dissatisfied occupant groups. We used a new analytical approach on 36671 post-occupancy evaluation responses to overcome this problem and better understand workspace satisfaction in office buildings. Principal components analysis reduced satisfaction votes with 15 different IEQ items into two principal components related to: 1) privacy and amount of space, and 2) cleanliness and maintenance. We grouped the data by occupants that were either satisfied or dissatisfied with their workspace. Principal component 1 explained half of the variability in the dataset and reliably distinguished occupants satisfied with their workspace from those that were dissatisfied. We used support vector machine to classify the satisfied and dissatisfied groups based on principal components 1 and 2. Classification of occupant satisfaction with the overall workspace was highly accurate (approximately 90%) and based predominantly on the component related to privacy and amount of space. Further analyses showed that occupants satisfied with their overall workspace were generally satisfied with all other IEQ items. There was greater independence between workspace attributes for those dissatisfied with their overall workspace. Issues of privacy and available space were an overwhelming determinant of occupant dissatisfaction irrespective of the success of other workspace attributes. These findings suggest that efforts to improve occupant satisfaction with workspaces should leverage designs that ensure privacy and provide sufficient space to support occupants in their work.

Research into human thermal perception indoors has focused on ‘neutrality’ under steady-state conditions. Recent interest in thermal alliesthesia has highlighted the hedonic dimension of our thermal world that has been largely overlooked by science. Here, we show the activity of sensory neurons can predict thermal pleasure under dynamic exposures. A numerical model of cutaneous thermoreceptors was applied to skin temperature measurements from 12 human subjects. A random forest model trained on simulated thermoreceptor impulses could classify pleasure responses (F1-score of 67%) with low false positives/negatives (4%). Accuracy increased (83%) when excluding the few extreme (dis)pleasure responses. Validation on an independent dataset confirmed model reliability. This is the first empirical demonstration of the relationship between thermoreceptors and pleasure arising from thermal stimuli. Insights into the neurophysiology of thermal perception can enhance the experience of built environments through designs that promote sensory excitation instead of neutrality.

The layout of ceiling fans in buildings is challenging because of the need to co-ordinate with other elements in the ceiling space, and because the resulting airflows within the occupied space interact with furniture. This study conducted detailed air speed measurements in four buildings with different room sizes, furniture configurations, ceiling fan types, and ceiling-fan-to-floor-area ratios. We measured air speeds across the occupied spaces at four heights while varying ceiling fan operation modes such as fan rotational speed, operating direction, and the number of operating fans. In total, we collected 207,080 air speed samples at 343 sites under 20 test conditions. This paper presents the magnitude and distribution of air speeds, cooling effects, and their influencing factors. The Airspeed Coverage Index (ACI= (Fan air speed (SF)× Fan diameter (D))/√(Average area served per ceiling fan (A))) describes the combined effects of multiple influencing factors on the magnitude of air speed. ACI is employed to predict the average air speed and occupant cooling effect, yielding regression confidences higher than 0.95. When designing a space to a target airspeed or cooling effect, the ACI can help to determine parameters such as fan density required for fan choices. The measured data are compared with predictions from the CBE fan tool that had been developed from laboratory tests under simplified conditions. The comparison displays the blocking effects of the furniture, lowering the average air movement in the space, as well as reducing the air movement at the ankle level while increasing it at higher heights. The blocking effect increases with the density of the furniture. We also visually present fan interactions in which triplets of fans are arranged linearly or diagonally, showing that the diagonal layout of ceiling fans increases average air speed and improves its uniformity.

Occupant satisfaction surveys are widely used in laboratory and field research studies of indoor environmental quality. Field studies pose several challenges because researchers usually have no control over the indoor environments experienced by building occupants, it is difficult to recruit and retain participants, and data collection methods can be cumbersome. With this in mind, we developed a survey platform that uses real-time feedback to send targeted occupant surveys (TOS) at specific indoor environmental conditions and stops sending survey requests when collected responses reach the maximum surveys required. We performed a pilot study of the TOS platform with occupants of a radiant heated and cooled building to target survey responses at 16 radiant slab surface (infrared) temperatures evenly distributed from 15 to 30 °C. We developed metrics and ideal datasets to compare the TOS platform against other occupant survey distribution methods. The results show that this novel method has a higher approximation to characteristics of an ideal dataset; 41% compared to 23%, 19%, and 12% of other datasets in previous field studies. Our TOS method minimizes the number of times occupants are surveyed and ensures a more complete and balanced dataset. This allows researchers to more efficiently and reliably collect subjective data for occupant satisfaction studies.

Thermal comfort standards have suggested a number of physical indices which can be calculated from either building simulations or in situ physical monitoring to assess the long-term thermal comfort of a space. However, the prohibitively high cost of sensor technologies has limited the applications of these physical indices, and their usefulness has never been established using data collected in real buildings. This paper is the first assessment of the six types of existing indices (23 total) found in standards and five types of new indices (36 total) and their correlation with the long-term thermal satisfaction of building occupants. Correlation analyses were based on continuous thermal comfort measurements and post-occupancy evaluation surveys from four air-conditioned office buildings in Sydney, Australia. We found that the majority of existing indices, especially those based on predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) metrics, have a weak correlation with thermal satisfaction. The percentage of time outside a temperature range was the best-performing index from the standards (r=-0.63). A new index based on the percentage of time that daily temperature range is greater than a threshold reported the strongest correlation (r=-0.8) with thermal satisfaction for this dataset. The results suggest that occupants’ long-term thermal comfort is influenced more by pronounced excursions beyond some acceptable temperature range and large variations in daily temperature than the average experience over time. These findings support the use of continuous monitoring technologies for long-term thermal comfort evaluation and inform potential amendments of international thermal comfort standards.

Built spaces have the potential to influence a diverse set of factors like health, well-being, productivity, and social connection. However, most post occupancy evaluations (POE) focus solely on measuring people’s levels of comfort and satisfaction with their indoor environment. With increasing attention and interest in occupant wellness and productivity, there is a need to reassess whether occupant surveys are evaluating all they need to. One of the most widely used online POE tools is the Center for the Built Environment’s Occupant Survey. We analyzed data collected from this tool over the last two decades (>90,000 respondents from ~900 buildings) to summarize the database and evaluate the survey structure. 68% of the respondents are satisfied with their workspace. People are most satisfied with spaces’ ease of interaction (75% satisfied), amount of light (74%) and the cleanliness (71%), and most dissatisfied with sound privacy (54% dissatisfied), temperature (39%) and noise level (34%). Correlation, principal component, and hierarchical clustering analyses identified seven distinct categories of measurement within the 16 satisfaction items. Results also revealed that some items correlate strongly with one another, suggesting reduction in the scale may be possible. Based on these results, we discuss potential improvements and new directions for the future of POE tools.

The recent release of the largest database of thermal comfort field studies (ASHRAE Global Thermal Comfort Database II) presents an opportunity to perform a quality assurance exercise on the first generation adaptive comfort standards (ASHRAE 55 and EN15251). The analytical procedure used to develop the ASHRAE 55 adaptive standard was replicated on 60,321 comfort questionnaire records with accompanying measurement data. Results validated the standard's current adaptive comfort model for naturally ventilated buildings, while suggesting several potential nudges relating to the adaptive comfort standards, adaptive comfort theory, and building operational strategies. Adaptive comfort effects were observed in all regions represented in the new global database, but the neutral (comfort) temperatures in the Asian subset trended 1–2°C higher than in Western countries. Moreover, sufficient data allowed the development of an adaptive model for mixed-mode buildings that closely aligned to the naturally ventilated counterpart. We present evidence that adaptive comfort processes are relevant to the occupants of all buildings, including those that are air conditioned, as the thermal environmental exposures driving adaptation occur indoors where we spend most of our time. This suggests significant opportunity to transition air conditioning practice into the adaptive framework by programming synoptic- and seasonal-scale set-point nudging into building automation systems.

Air movement from fans is an effective way to deliver thermal comfort in warm air temperatures. We measured air speeds in a shared office at 15 siteswhere an occupant would typically be located. The fan speed and direction were changed to operate in either the upwards or downwards direction. Mean airspeeds in the occupied zone were higher when fans were blowing downwards, but the spatial distribution across the space was less uniform. When fans areblowing upwards, thermal comfort estimates using SET indicate less risk of discomfort from high airspeed locations directly under the fans compared withthe downward case. Vertical air speed gradients showed higher air speeds at head height and lower air speeds at ankle height in the upwards direction, butthe opposite profile for fans blowing in the downward direction. The positive vertical gradient in the upwards direction is favorable to reduce the potential fordraft at the ankles. These results suggest that despite lower air speeds, fans blowing upwards can provide more spatially uniform thermal comfort underelevated air movement, requiring less consideration of occupant and furniture placement relative to the fan.

The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15-25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants’ votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

Current thermal comfort standards use Predicted Mean Vote (PMV) classes as the compliance criteria despite previous critiques. The implicit assumption is that a narrower PMV range ensures higher thermal acceptability among building occupants. However, our analysis of a global database of thermal comfort field studies demonstrates that PMV classes are not appropriate design compliance criteria, and reinforces the need for a new and robust approach to thermal comfort compliance assessment. We compared two statistical methods to derive acceptable temperature ranges from occupant responses applied one to the ASHRAE Global Thermal Comfort Database II. Derived acceptable temperature ranges in real buildings (7.4K-12.2K) using this new method are wider than the current standards mandate (2K-6K). Our findings support the call for a relaxation of suggested temperature ranges in thermal comfort standards so as to minimize unnecessary space conditioning. The proposed data-driven statistical methods to determine temperature design compliance criteria are viewed as an important step forward in the age of continuous and pervasive monitoring and the associated large databases of building comfort measurements.

Addressing two common challenges for building performance – reducing the carbon footprint attached to the provision of comfortable indoor environments, and improving the health and wellbeing of occupants – requires a more comprehensive understanding of how the indoor environments of buildings are operated. This paper introduces SAMBA, a state-of-the-art monitoring station for continuous, real-time measurements of indoor environmental quality (IEQ) parameters from occupants’ work desks. It combines a hardware solution that integrates a low-cost suite of sensors with a software platform designed to automatically analyse and visualize data for quick interpretation of IEQ performance by non-scientist. In addition to feeding a massive IEQ database for research, the resulting data may be used to better inform the metrological requirements for popular international IEQ rating schemes. This new era of indoor environmental monitoring based upon systems such as SAMBA affords a fundamentally new approach to built environmental field research that holds significant promise to improve building performance and indoor environmental quality and occupant satisfaction, health, wellbeing and performance.

The quality of buildings can be assessed in terms of the indoor air quality, thermal comfort, lighting quality, acoustic comfort afforded the occupants, collectively referred to as Indoor Environmental Quality (IEQ). A major barrier to a more thoroughly representative audit of actual IEQ performance are the expense and complexity of the measurement instrumentation required. Rapid developments in sensor technology in recent years present the opportunity for continuous and pervasive IEQ monitoring to deliver truly representative characterisations of building performance at a modest cost. The last remaining obstacle to realising these developments seems to be a concern about instrument accuracy. In this paper we test the performance of a low-cost IEQ monitoring system (SAMBA) introduced in an earlier paper. Calibration data from 100 devices was analysed to calculate the standard error of the estimate as a measure of equipment accuracy. Those performance specifications were used in a Monte Carlo simulation based on measurements of thermal comfort parameters from 24 office buildings. Performance measures suggests the low-cost system, whilst not as accurate as laboratory equipment, is more than sufficient for building IEQ diagnostics and compliance assessments. Furthermore, the results of the Monte Carlo simulation show that continuous monitoring systems are better at characterising long-term performance than ad hoc measurement strategies using precision equipment. Low-cost pervasive monitoring technologies therefore offer a unique opportunity to improve our quantitative understanding of, and response to, indoor environmental quality issues.

Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley’s Center for the Built Environment and The University of Sydney’s Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying “right-here-right-now” subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web-based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects’ demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data.

The classic or deterministic understanding of thermal comfort contends that it is driven exclusively by the physics of the body’s heat exchange with its immediate thermal environment. But in recent decades there has been widespread recognition that a person’s thermal history and adaptation level, including behavioral, physiological and psychological processes operating on various timescales, also influence whether indoor climatic conditions will be deemed comfortable and acceptable, or not, as the case may be. This contextual view of the comfort construct is referred to as the adaptive model, and its empirical basis is an extensive body of field studies conducted in diverse occupancy and building types located across a broad cross-section of the world’s climate zones. The basic research design of the comfort field study consists of paired observations of objective indoor climatic parameters and their subjective evaluation by building occupants using comfort rating scales. The first global adaptive comfort standard, ratified by ASHRAE in 2004, was based on a statistical analysis of approximately 21,000 complete sets of objective indoor climatic observations and their “right-here-right-now” subjective evaluations by the building occupants who were exposed to them. Over a decade has elapsed since ASHRAE Standard 55-2004 was published and since then there has been an escalation in adaptive comfort field research activity around the world. Recently ASHRAE’s thermal comfort technical committee (TC 2.1), in collaboration with the International Energy Agency (Annex 69), have taken on the challenge of systematically collating and harmonizing the raw data collected in recent comfort field studies from around the world into an expanded, quality-assured database. When the project is completed this public-domain resource will underpin myriad inquiries by the thermal comfort community, researchers and practitioners alike. The aim of this presentation is to describe the DB-II project, including the principles guiding the collation and quality assurance of its massive volume of raw input data, and also pointing to some of the new opportunities it will afford the research community over coming years.