According to a recent survey, about half of Americans feel their office is either too hot or too cold.
A number of factors play into this issue of thermal comfort, but the hardest factor to control for is the one we’re most interested in: humans themselves.
Clothing choice and body shape are intrinsically tied to what temperature an individual will be most comfortable at.
Carnegie Mellon University’s Mario Bergés believes that our environments should be more adaptive and consider not just our presence, but also our physical characteristics, clothing, and thermal comfort preferences when deciding how to condition the spaces we are in.
Bergés, a professor of civil and environmental engineering, and his collaborators have created the first model that combines environmental information with data on an individual’s body shape to determine at what temperatures that person will feel most comfortable.
That information is then aggregated to find the temperature that most occupants possible will find comfortable, in a system his team has dubbed OccuTherm.
Crucially, the system can estimate body shape information from depth-imaging sensors mounted on doorways, which is less privacy-invasive than other sensing approaches such as traditional cameras.
As Bergés and fellow researchers note in a recent paper, thermal comfort has a significant effect on the physiological and psychological well-being of an individual and affects occupants’ health, satisfaction, and performance.
Studies have shown that optimal thermal comfort conditions can lead to an increase in concentration and productivity, while poor thermal comfort conditions can lead to lethargy and distraction.
OccuTherm works by estimating the circumference of an individual’s shoulders from above, then combining it with height and weight estimates to infer the optimal temperature for that individual’s comfort.
Just as society has come a long way in embracing differences that were once brushed over or ignored, this system demonstrates the need to acknowledge that each person has a different body shape, with different needs that must be met on their own terms.
It incorporates a much deeper level of human understanding than traditional heating and cooling methods, which use generic set-temperature models that have been in place for decades.
OccuTherm is a dynamic system that personally factors each individual entering or leaving a room into the system’s temperature setting.
OccuTherm is six percent more effective than leading state-of-the-art approaches, which may also employ restrictive wearables or require constant user interaction.
The system, the authors note, “works without the need for frequent user comfort feedback reports and leverages data from depth-imaging sensors, which are quickly becoming commonplace in indoor environments.”
A smarter temperature control system like OccuTherm could also help reduce energy costs and, by extension, carbon emissions. About 50 percent of energy used in human-occupied spaces is expended on heating, cooling, and ventilation. Thermostats are usually turned to a set temperature for the whole building, even if most of the area is unoccupied. Eliminating wasteful, outdated methods and putting the individual needs of the occupants first could not only equal a more content and productive workplace, but also major savings for both managers and the environment.
Bergés and his colleagues plan to integrate more advanced features as the system continues to improve that can also take into account an individual’s clothing choices. Allowing users to provide direct feedback could also further refine OccuTherm’s ability to help people reach optimal thermal comfort.
Ultimately, the system will help close the gap between human needs and the spaces built to satisfy them, providing a more comfortable and livable environment for all.
“Buildings can do a better job in keeping us comfortable while using less energy, and we have a lot to learn still, including replicating this study on a larger population, says Bergés.
“These results are promising and show that there’s potential for non-intrusive technologies to obtain comfort-predictive attributes for occupants.”
The OccuTherm research team includes doctoral candidates Jonathan Francis of Carnegie Mellon’s School of Computer Science, Matias Quintana of The National University of Singapore, and Nadine von Frankenberg of the Technical University of Munich, as well as Senior Research Scientist Sirajum Munir from the Bosch Research & Technology Center.
An accurate control and design strategy for the indoor thermal environment is necessary to improve the comfort, well-being and general health of building occupants, as well as to boost their work performance1,2, and limit building energy consumption3,4. Conventionally, the operation of air-conditioning systems has been based on the heat balance model for thermal comfort developed in the 1970s for air-conditioned buildings5 and extended later for non-air-conditioned buildings in warm climates6.
The model predicts the human thermal sensation by generating a numeric value called the “predicted mean vote” (PMV) that ranges from cold to hot as a function of physical parameters (i.e., air temperature, mean radiant temperature, air velocity and humidity) and of personal factors (i.e., metabolic rate and clothing insulation).
Another well-known model suggested for the design and control of the thermal environment in non-air-conditioned buildings (i.e., naturally ventilated), is based on adaptive theory. Adaptive theory considers occupants as active users who enact changes to their thermal environment via behavioural adjustments (e.g., opening windows and use of blinds and fans), physiological adaptation (i.e., acclimatization) and psychological expectation and habituation in relation to the indoor and outdoor environment7–9.
Based on the adaptive theory, the standardised adaptive model10–12 relates the acceptable ranges of indoor temperature to outdoor temperatures, supposing an expectation and habituation process, and is derived from data mined in naturally ventilated buildings from field studies (i.e., an in situ polls on comfort evaluations together with measurements of the actual indoor conditions)13.
The application of the adaptive model instead of the heat balance model allows for an extended acceptable range of temperatures in buildings, resulting in substantial reduction of energy consumption14.
In addition, the adaptive model could help to explain the gap observed in field studies between the thermal sensation vote reported by people (i.e., “actual sensation vote”, asv) and the calculated PMV7,15.
To further expand the temperature range to be used in buildings and reduce the asv-PMV gap (i.e., “actual sensation vote” – “predicted mean vote” gap), other types of adaptation processes have been proposed as an extension of the current adaptive model or as integration to the heat balance model16–18.
To date, despite having been identified as likely influencing parameters of occupants’ thermal sensation7, the indoor environmental parameters not related to thermal conditions (i.e., light, acoustic ambiance and air quality) have never been included in thermal comfort models.
The thermal sensation predicted by thermal comfort models is just one of the possible “thermal responses” that could result from an environmental stimulus. In the context of this research, we consider thermal sensation as part of the “subjective thermal perception”, together with thermal comfort, preference and acceptability.
Researchers have suggested that thermal sensation is the first subjective response to the physical environment19, and that it is strongly correlated with physiological responses20. On the other hand, thermal preference, comfort and acceptability have been considered to be the results of the reflection upon the sensation, i.e., they are thermal evaluations19.
The relationship between thermal sensation and thermal evaluations has been explained physiologically with the concept of alliesthesia21,22 and psychologically19 relying on expectations, adaptations and other factors7,19,20,23. In this study, together with the aforementioned subjective thermal perception ratings, physiological responses will be evaluated as additional thermal responses.
The simultaneous presence of thermal and non-thermal factors in indoor spaces can result in two different effects: combined effects when the overall perception of the indoor environment is affected by the combined presence of multiple indoor factors16,24–26, and cross-modal effects when thermal responses (intended as both subjective thermal perception and physiological responses) are influenced by factors not related to the thermal environment (i.e., non-thermal factors).
Despite the potential application of results on cross-modal effects in all indoor spaces due to the simultaneous presence of multiple environmental stimuli, there is a lack of integration of non-thermal factors into thermal comfort models. This is the consequence of a limited knowledge on the interactions of indoor factors27.
Among the possible interactions of indoor factors, the study of the effect of light conditions on thermal responses has been the most investigated27, considering the large implications that results could have on energy consumption of buildings28 due to the possibility of fine-tuning the thermal environment according to the lighting conditions.
In addition, many studies on the cross-modal effect of light on thermal responses have been conducted in relation to non-image forming effects of light29, following the discovery of a new photoreceptor in the human eye30. In this context, the effect of electric light quantity on physiological responses (e.g., core body temperature and skin temperature) has been largely investigated in controlled laboratory experiments29.
Findings from these studies show that exposure to bright light in the evening affects the human thermoregulation rhythm by delaying the natural decline of the core body temperature while simultaneously slowing down the increase of distal skin temperature29. On the other hand, it has been observed that an exposure to bright light in the morning results in a faster increase of core body temperature29.
Only a handful of studies have investigated the effect of light on physiological responses together with subjective thermal perceptions31–36 or focused on subjective thermal perceptions alone28,37,38. As for investigations on physiological responses only, all of these studies investigated the effects of variations of electric light quantity. It has been hypothesised that the light could have an indirect effect on subjective thermal perceptions through variations of human thermoregulation rhythm (observed with changes in physiological responses)29. A correlation between time of exposure, light intensity, core body temperature, and subjective thermal sensation was found in some studies that investigated both physiological responses and subjective thermal perception31–33.
In particular, it has been reported that after a bright light exposure during the daytime, the core body temperature was lower and the thermal sensation was warmer compared to an exposure to dim light. On the other hand, after a bright light exposure at night, the core body temperature was higher and the thermal sensation was colder compared to dim light. A cooler thermal sensation after exposure to dim light compared to bright light was also confirmed in experiments reporting only subjective thermal sensation, with exposure during the day37 or at an unknown time of the day38.
However, contradictory35 or inconclusive28,34,36 results have been reported in other studies investigating subjective thermal perceptions only. These incongruities can be explained in terms of different experimental design features such as the timing and duration of the light exposure or the light intensity29. For example, the use of a too low light intensity for the bright light exposure can lead to inconclusive results36.
We also suggest that differences between the time of the light exposure and that of the measurement should be considered. In other words, do they correspond?
If not, what is the thermal environment in both of them? What is the visual environment? Furthermore, the type of thermal environment should be taken into account (e.g., static vs. dynamic or comfortable vs. not comfortable) and the type of thermal perception investigated (i.e., thermal acceptability was never investigated).
Based on these considerations, it is clear that further investigations are necessary for the understanding of the relationship between light, subjective thermal perceptions and physiological responses according to different timing and duration of the light exposure and variations in the thermal environment.
Considering that all previous investigations relied on electric lighting29,34, there is a lack of knowledge on how daylight affects thermal responses, intended as both subjective thermal perception and physiological responses. Only one study by the authors examined the effect of daylight on subjective thermal perceptions (i.e., thermal sensation and thermal evaluation), but in field studies39 where many parameters could not be controlled but only measured, and where physiological measurements were not collected.
The use of natural light instead of electric light, besides causing variations in the thermal environment due to physical changes (i.e., heat gains and losses through the glazing), might result in different and additional psychological effects, as it has been reported in previous visual comfort studies40,41, especially considering that the concept of light-warmth and darkness-cold is intrinsic to the human psyche38. As an example of this psychological effect of daylight on subjective thermal perception, it has been hypothesised that the thermal environment in a naturally-lit space could be more tolerated than what is predicted by the PMV of the heat balance model42. Given the large presence of daylight in buildings and the tendency to design highly glazed façades, studying the implications of daylight on thermal responses is becoming even more of an urgent problem to tackle. However, considering the number of confounding factors combined with the inability to control daylight conditions in field studies39 (e.g., illuminance levels can only be measured and not controlled or changed, or blinds position are defined by the users), it is necessary to conduct such investigations in a controlled environment if one aims to identify specific effects or interactions with the thermal environment.
It must be remarked, however, that in experiments with daylight (even when performed in a controlled environment), not all possible visual parameters can be controlled due to the contact with the outside and the changes of daylight positions over the day and the year. Acknowledging this limitation, in the context of this research, a daylight experiment is defined “controlled” whenever some conditions of the visual environment (e.g., illuminance levels, blinds position, type of view to the outside, colour of the glazing) can be decided, changed and kept (semi-)constant, contrary to field studies.
Addressing the challenges highlighted in the analysed literature, this study reports the results of a controlled experimental study investigating the influence of daylight on thermal responses, intended as both subjective thermal perceptions (i.e., thermal sensation and thermal evaluations) and physiological responses (i.e., skin temperature), by studying different combinations of daylight and temperature levels.
As examined for electric light in past studies, different daylight levels (i.e., low, medium and high illuminance levels) were studied to explore their cross-modal effect on thermal responses. In addition, to account for interactions (i.e., to consider under which thermal environment cross-modal effects occur), the same investigation was performed at three temperature levels. This also allowed the study of the combined effects of daylight and temperature levels on overall comfort.
It was hypothesised that:
(i) thermal responses are affected by daylight, and that they would be lower (i.e., colder and less acceptable for thermal perception) under low illuminance versus high illuminance daylight levels (cross-modal main effect);
(ii) the cross-modal effect depends on the thermal environment people are exposed to (cross-modal interaction effect);
(iii) daylight and temperature contribute equally to the evaluation of the indoor environment as a whole (combined effect);
(iv) the thermal environment in a warm condition is more tolerated when daylight is present compared to the evaluation of the same thermal environment in an electrically-lit space. For the last point, analysis of the consideration of the visual environment into thermal comfort models will be presented.
This analysis will focus on the comparisons between the calculated PMV according to measured indoor environment parameters and the metabolic rate and clothing insulation of participants and the thermal sensation rating (asv) reported by participants.
More information: Jonathan Francis et al. OccuTherm, Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (2019). DOI: 10.1145/3360322.3360858