A personal comfort model is a new approach to thermal comfort modeling that predicts an individual's thermal comfort response, instead of the average response of a large population. It leverages the ...Internet of Things and machine learning to learn individuals' comfort requirements directly from the data collected in their everyday environment. Its results could be aggregated to predict comfort of a population. To provide guidance on future efforts in this emerging research area, this paper presents a unified framework for personal comfort models. We first define the problem by providing a brief discussion of existing thermal comfort models and their limitations for real-world applications, and then review the current state of research on personal comfort models including a summary of key advances and gaps. We then describe a modeling framework to establish fundamental concepts and methodologies for developing and evaluating personal comfort models, followed by a discussion of how such models can be integrated into indoor environmental controls. Lastly, we discuss the challenges and opportunities for applications of personal comfort models for building design, control, standards, and future research.
•Framework for personal comfort models to predict individuals' thermal comfort.•Literature review on personal comfort models.•Methodologies that leverage the Internet of Things and machine learning.•System architecture for integrating personal comfort models in indoor environmental controls.•Challenges and opportunities for model applications in design, control, standards, and future research.
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.
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•Assessed PMV-PPD accuracy using the ASHRAE Global Thermal Comfort Database II.•PMV predicted thermal sensation correctly only one out of three times.•PMV had a mean absolute error of one unit on the thermal sensation scale.•PPD was not able to predict the dissatisfaction rate.•PMV-PPD accuracy varied strongly between ventilation, building types and climate.
A personal comfort model is a new approach to thermal comfort modeling that predicts individuals' thermal comfort responses, instead of the average response of a large population. However, securing ...consistent occupant feedback for model development is challenging as the current methods of data collection rely on individuals' survey participation. We explored the use of a new type of feedback, occupants' heating and cooling behavior with a personal comfort system (PCS) for the development of personal comfort models to predict individuals' thermal preference. The model development draws from field data including PCS control behavior, environmental conditions and mechanical system settings collected from 38 occupants in an office building, and employs six machine learning algorithms. The results showed that (1) personal comfort models based on all field data produced the median accuracy of 0.73 among all subjects and improved predictive accuracy compared to conventional models (PMV, adaptive) which produced a median accuracy of 0.51; (2) the PMV and adaptive models produced individual comfort predictions only slightly better than random guessing under the relatively mild indoor environment observed in the field study; and (3) the models based on PCS control behavior produced the best prediction accuracy when individually assessing all categories of field data acquired in the study. We conclude that personal comfort models based on occupants' heating and cooling behavior can effectively predict individuals' thermal preference and can therefore be used in everyday comfort management to improve occupant satisfaction and energy use in buildings.
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•We propose a new modeling approach for thermal comfort – personal comfort models.•We employ machine learning to predict individuals' thermal preference.•Individuals' models improve prediction accuracy compared to PMV and adaptive models.•Occupant heating and cooling behavior is a strong comfort predictor.
•Office plug load field study suggests energy saving opportunities through games.•Non-financial incentives may motivate occupants to change energy use behaviors.•Office workspace plug load energy ...consumption is strongly linked to occupancy.
This study evaluates the energy patterns of 137 individual plug loads (desktops, laptops, monitors, and task lights) collected in a California office building over two years, and the effects of a behavior-based intervention on a subset of these devices to reduce plug load energy consumption. An analysis of the data reveals that desktops consume the most power per person and demonstrate the widest range of power consumption, and that occupants are more likely to turn equipment off before a longer break from the office than overnight during the week. Much of the literature on reducing commercial plug loads is focused on technology-based solutions, while the literature on changing occupant behavior is focused on residential occupants. Multiple studies show that non-financial incentives, such as games, can motivate behavior change. An online sustainability game, Cool Choices, was initiated on-site with 30 occupants, where players competed on teams to earn points for completing resource-saving actions. The analysis revealed that because occupants were already engaging in relevant energy saving behaviors (e.g. turning equipment off at the end of the day), there was limited opportunity for further behavior-based reductions. This study highlights the need for additional research in commercial buildings examining how to motivate occupant behavior change through non-financial incentives.
•The natural ventilation reduction varies from 5% to 70% across the US.•Most cities have less than 20% reduction except for four cities in the US.•PM2.5 is the most significant outdoor air pollutant ...compared to PM10 and Ozone.•We have developed an advanced indoor air pollutant control with emulators.
Natural ventilation has emerged as a desirable feature of sustainable buildings given its potential positive impact on building energy performance and amenities for occupants. In some circumstances, natural ventilation can provide a higher ventilation rate compared to mechanical ventilation, thus improving the air quality of indoor space, resulting in lower indoor carbon dioxide and volatile organic compound concentrations. However, this increased ventilation rate also raises the issue of increased indoor pollutant concentration from outdoor sources, which has been proven to significantly affect occupant health. In this paper, we investigate the influence of three major outdoor air pollutants, PM2.5, PM10, and ozone, on natural ventilation usage across 12 major US cities and their corresponding climate zones utilizing the outdoor air pollutant records from the US Environmental Protection Agency. Firstly, a descriptive statistics study presents a general description of air pollutant records in these investigated cities and climate zones. Then two natural ventilation operation scenarios (considering outdoor air pollutants vs. not) are developed and compared to explicitly show the expected natural ventilation reduction in these areas. The investigation results indicate that the most polluted area for natural ventilation is Los Angeles (with a projected 70% reduction), followed by Chicago (approximately 40% reduction), and then Atlanta and San Francisco (20–30% reduction for each). The natural ventilation reduction caused by outdoor air pollutants ranges from 5% to 20% in all other tested cities. Among the three pollutants (PM2.5, PM10, and ozone) we investigated, the influence of PM2.5 consistently emerges as the most critical to consider, while the impact of PM10 is typically trivial. The influence of ozone is not obvious in most cases. Nevertheless, in certain cases, its influence is non-negligible when natural ventilation is utilized. This study aims to provide a general guideline for decision makers to consider the influence of outdoor air quality on natural ventilation usage when adopting natural ventilation in different US locations. The results also confirmed the outdoor air pollutants, especially PM2.5, as a significant factor to consider in the natural ventilation design to shield the occupant from excessive air pollutant exposure.
Advances in heating, ventilation and air conditioning (HVAC) technologies have dramatically improved the indoor thermal environment, but attention should be paid on how this would affect building ...occupants' thermal comfort perception. In this paper, we studied the mutually dependent relationship between indoor climate experience and occupants' comfort expectation. An intriguing experiment was conducted in China where wintertime indoor thermal environments in northern cities (with district heating) are much warmer than in southern region (without district heating). By analyzing the 4411 responses from four college-aged subject groups with different indoor thermal history, two interesting findings emerged. Firstly, people's understandings of thermal comfort change with their indoor thermal experiences. Those permanently live in lower-grade non-neutral thermal environment can achieve similar thermal comfort perception as those who live in long-term comfortable thermal conditions. Secondly, the dynamics of building occupants' thermal comfort adaptation project asymmetric trajectories. It is much quicker for occupants to accept neutral indoor climate than to lower their expectation and adapt to under-conditioned environments. These two phenomena can be well described by the index “demand factor”, which can serve as a reference for future thermal comfort study.
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•This study explores indoor climate experience and thermal comfort expectation.•4411 responses from subject groups with different indoor thermal history were collected.•The results show that people's understandings of thermal comfort are malleable.•The dynamics of building thermal comfort adaptation exhibit asymmetric trajectories.•The index “demand factor” was adopted to describe expectation dynamics.
Visual connection to nature has been demonstrated to have a positive impact on attention restoration, stress reduction, and overall health and well-being. Inside buildings, windows are the primary ...means of providing a connection to the outdoors, and nature views even through a window may have similar effects on the occupants. Given that humans recognize environments through multi-sensory integration, a window view may also affect occupants’ thermal perception. We assessed the influence of having a window with a view on thermal and emotional responses as well as on cognitive performance. We conducted a randomized crossover laboratory experiment with 86 participants, in spaces with and without windows. The chamber kept the air and window surface temperature at 28 °C, a slightly warm condition. The outcome measures consisted of subjective evaluations (e.g., thermal perception, emotion), skin temperature measurements and cognitive performance tests. In the space with versus without windows, the thermal sensation was significantly cooler (0.3 thermal sensation vote; equivalent to 0.74 °C lower), and 12% more participants were thermally comfortable. Positive emotions (e.g., happy, satisfied) were higher and negative emotions (e.g., sad, drowsy) were lower for the participants in the window versus the windowless condition. Working memory and the ability to concentrate were higher for participants in the space with versus without windows, but there were no significant differences in short-term memory, planning, and creativity performance. Considering the multiple effects of window access, providing a window with a view in a workplace is important for the comfort, emotion, and working memory and concentration of occupants.
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•We conducted a randomized crossover experiment with 86 participants.•At a slightly warm condition, people felt cooler and more comfortable with windows.•Positive emotions increased while negative emotions decreased with windows.•Participants' working-memory and concentration improved in a space with windows.•Having windows did not affect short-term, planning and creativity performance.
The current Indian indoor comfort standards do not reflect the country's great cultural and climatic diversity. There have been very few reports on the actual environments in Indian offices in the ...last three decades. We conducted a thermal comfort field study in 28 naturally ventilated (NV) and air-conditioned (AC) offices in Chennai and Hyderabad for fourteen months, and collected 6048 responses from 2787 individuals. Warm humid and composite climates are experienced in these cities, and these two climates cover about 80% area of the country.
This paper proposes an adaptive thermal comfort model for South India based on this data. Mean comfort temperature was found to be 28.0 °C in NV mode, and 26.4 °C in AC mode on all data. Chennai had slightly higher comfort temperature. We found an adaptive relationship between the prevailing outdoor temperature and the comfortable indoor temperatures. Most of the environments in NV mode and about half in AC mode were warmer than the current Indian Standard upper limit (26 °C).
In most cases, the air speed was below 0.20 m/s. Most of the subjects used fans. Air speeds of 1 m/s increased the comfort temperature by 2.7 K in both the modes. Logistic regression predicted 87% and 50% fan usage at 29 °C in NV and AC modes respectively. Several factors prevented further thermal adaptation. We can potentially improve comfort and reduce air-conditioning by providing higher air speeds with energy-efficient fans. Such strategies may be vital given the scale of the scarcity of power.
•We did a thermal comfort survey in 28 offices in Chennai and Hyderabad for 14 months.•We found the adaptive comfort model for South Indian offices as Tcomf = 0.26 Trm + 21.4.•Mean comfort temperatures of 28.0 and 26.4 °C in NV and AC modes were noted.•Mean indoor air speeds were below 0.20 m/s most of the time.•Air speeds of 1 m/s increased the comfort temperature by 2.7 K in both the modes.
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.
Previous studies have demonstrated a potential reduction in cooling load and improvement in comfort from the implementation of night ventilation. This paper describes the performance, in terms of ...indoor environmental conditions, of three buildings from both the U.S. and India that use night ventilation as their primary cooling method. The research methods used the following approach: (1) Assess the cooling strategy in relation to the adaptive comfort model; (2) Develop a hybrid model, using both first principle equations and the collected data, to predict the instantaneous air and mass temperatures within each building and use the model to assess performance of the cooling strategy; (3) Determine an optimized ventilation control strategy for each building to minimize energy and maintain comfortable temperatures. (4) Develop a statistical model using collected data to predict the window opening pattern for occupants of a building using natural night ventilation. The study yielded the following results: (1) The buildings in the mild climate are successfully keeping the indoor temperature low, but also tend to be overcooling; (2) The night ventilation strategy has very little impact on indoor conditions of the buildings in the mild climate; (3) The impact of night ventilation is less significant when there is low internal loads and heavy mass; (4) The building in the hot and humid climate is keeping the indoor temperature within the comfort bounds for 88% of the year; (5) The night ventilation strategy has advantageous impact on indoor conditons of the building in the hot and humid climate, but not enough to cool the space on its own; (6) Model predictive control has the potential to further improve the performance of night ventilation. (7) Window opening behavior for the building using natural night ventilation is most heavily dependent on indoor air temperature and mass temperature.
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