We developed pythermalcomfort, a Python package that allows users to calculate the most common thermal comfort indices in compliance with the main thermal comfort standards. For example, pythermalcomfort can be used to calculate: whole body thermal comfort indices (e.g., Predicted Mean Vote, adaptive models, Standard Equivalent Temperature), local discomfort, clothing insulation, and psychrometric properties of air. All pythermalcomfort functions have been validated against the reference tables provided in the corresponding thermal comfort standards. We have developed documentation, examples and tutorial videos to guide users on how to use our package. pythermalcomfort allows researchers and professionals to accurately perform complex thermal comfort calculations without the need of re-writing the programming code. With Python being among the most widely utilized programming languages and pythermalcomfort being the only Python library which includes a comprehensive list of thermal comfort functions, we believe that pythermalcomfort will have a significant impact on both the research and industrial communities.

There is limited scientific evidence on what is the optimal sampling period to measure skin temperature in longitudinal thermal comfort studies, and how this sampling period selection affects the results. iButtons® are among the most widely used wireless sensors in field and lab studies to measure skin temperature, since they are accurate, reliable, and cause minimal discomfort. However, their use is significantly limited by their memory capacity. We aimed to determine what is the optimal sampling period of skin temperature in studies which use iButtons®. We measured wrist skin temperature of 14 participants at 60 s intervals for a period of 1 month and wrist temperature of 5 participants at 20 s intervals for a week. Results showed that the selection of a 300 s sampling period would provide reasonably accurate results while limiting the number of times data needs to be downloaded.

The Center for the Built Environment (CBE) Thermal Comfort Tool is a free online tool for thermal comfort calculations and visualizations that complies with the ASHRAE 55–2017, ISO 7730:2005 and EN 16798–1:2019 Standards. It incorporates the major thermal comfort models, including the Predicted Mean Vote (PMV), Standard Effective Temperature (SET), adaptive models, local discomfort models, SolarCal, and dynamic predictive clothing insulation. Our tool also provides dynamic and interactive visualizations of thermal comfort zones. The CBE Thermal Comfort Tool has several practical applications and each year is used by more than 49,000 users worldwide, including engineers, architects, researchers, educators, facility managers and policymakers.

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right-Here-Right-Now surveys using a smartwatch for 180 days. We collected more than 1080 field-based surveys per participant. Surveys were matched with environmental and physiological measured variables collected indoors in their homes and offices. We then trained and tested seven machine learning models per participant to predict their thermal preferences. Participants indicated 58% of the time to want no change in their thermal environment despite completing 75% of these surveys at temperatures higher than 26.6°C. All but one personal comfort model had a median prediction accuracy of 0.78 (F1-score). Skin, indoor, near body temperatures, and heart rate were the most valuable variables for accurate prediction. We found that ≈250-300 data points per participant were needed for accurate prediction. We, however, identified strategies to significantly reduce this number. Our study provides quantitative evidence on how to improve the accuracy of personal comfort models, prove the benefits of using wearable devices to predict thermal preference, and validate results from previous studies.

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using two personal comfort datasets containing longitudinal data from 55 people. The datasets comprise more than 6000 unique right-here-right-now thermal comfort surveys. The results show that a CCM that uses only the one-time onboarding survey information of an individual occupant has generally as good or better performance as compared to conventional general-purpose models, but uses no historical longitudinal data as compared to personalized models. If up to ten historical personal preference data points are used, CCM increased the thermal preference prediction by 8% on average and up to 36% for half of the occupants in the first of the tested datasets. In the second dataset, one-third of the occupants increased their thermal preference prediction by 5% on average and up to 46%. CCM can be an important step toward the development of personalized thermal comfort models without the need to collect a large number of datapoints per person.

Heatwaves are one of the most dangerous natural hazards causing more than 166,000 deaths from 1998–2017. Their frequency is increasing, and they are becoming more intense. Electric fans are an efficient, and sustainable solution to cool people. They are, for most applications, the cheapest cooling technology available. However, many national and international health guidelines actively advise people not to use them when indoor air temperatures exceed the skin temperature, approximately 35°C. We used a human energy balance model, to verify the validity of those recommendations and to determine under which environmental (air temperature, relative humidity, air speed and mean radiant temperature) and personal (metabolic rate, clothing) conditions the use of fans would be beneficial. We found that current guidelines are too restrictive. Electric fans can be used safely even if the indoor dry-bulb temperature exceeds 35°C since they significantly increase the amount of sweat that evaporates from the skin. The use of elevated air speeds (0.8m/s) increases the critical operative temperature at which heat strain is expected to occur by an average of 1.4°C for relative humidity values above 22%. We also analysed the most extreme weather events from 1990 to 2014 recorded in the 115 most populous cities worldwide, and we determined that in 103 of them the use of fans would have been beneficial. We developed a free, open-source, and easy-to-use online tool to help researchers, building practitioners, and policymakers better understand under which conditions electric fans can be safely used to cool people.

Periodic disinfection of workspaces can reduce SARS-CoV-2 transmission. In many buildings periodic disinfection is performed manually; this has several disadvantages: it is expensive, limited in the number of times it can be done over a day, and poses an increased risk to the workers performing the task. To solve these problems, we developed an automated decontamination system that uses ultraviolet C (UVC) radiation for disinfection, coupled with occupancy detection for its safe operation. UVC irradiation is a well-established technology for the deactivation of a wide range of pathogens. Our proposed system can deactivate pathogens both on surfaces and in the air. The coupling with occupancy detection ensures that occupants are never directly exposed to UVC lights and their potential harmful effects. To help the wider community, we have shared our complete work as an open-source repository, to be used under GPL v3.

Climate-responsive building design holds immense potential for enhancing comfort, energy efficiency, and environmental sustainability. However, many social, cultural, and economic obstacles might prevent the wide adoption of designing climate-adapted buildings. One of these obstacles can be removed by enabling practitioners to easily access, visualize and analyze local climate data. The CBE Clima Tool (Clima) is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design. It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices. It is aimed at students, educators, and practitioners in the architecture and engineering fields. Since its inception, Clima’s user base has exhibited robust growth, attracting over 25,000 unique users annually from across 70 countries. Our tool is poised to revolutionize climate-adaptive building design, transcending geographical boundaries and fostering innovation in the architecture and engineering fields.

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) and contextual factors such as who they were with and what activity they were doing. The results of 2,400 micro-surveys across various urban settings are illustrated in this paper, showing the variability of noise-related distractions, thermal comfort, and associated context. The results show that participants experienced at least a little noise distraction 58% of the time, with people talking being the most common reason (46%). This effort is novel due to its focus on spatial and temporal scalability and the collection of noise, distraction, and associated contextual information. These data set the stage for larger deployments, deeper analysis, and more helpful prediction models toward better understanding the occupants’ needs and perceptions. These innovations could result in real-time control signals to building systems or nudges for people to change their behavior.

Elevated air movement produced by fans can offset air-conditioning energy requirements by allowing temperature setpoints to be raised without compromising thermal comfort. These advantages are even greater in hot and humid climates that inherently have large and sustained indoor cooling requirements. Few studies have assessed the in-situ benefits of fans in actual buildings. We installed ceiling and desk fans into a Zero Energy office building (675 m2) in Singapore. Across an 11-week period, 35 occupants alternated between two conditions (no fan vs. fan): 24 °C setpoint with fans off, and 26.5 °C setpoint with fans on. When the temperature setpoint was raised and elevated air movement was provided, a 32% energy reduction was obtained. The energy savings accrued without any negative impacts occurring on thermal satisfaction. Overcooling caused by thermal preference to slightly warmer and warmer conditions was substantially reduced from 33 to 9%. No changes in perceived air-staleness or self-reported alertness and ability to concentrate occurred either, indicating parity across the no fan and fan conditions. Although occupants primarily relied on ceiling fans at the 26.5 °C setpoint, they were by default on at the beginning of each day, giving less incentive to use the desk fans. Our study took place in a high-performance Zero Energy building, whereby thermal dissatisfaction was already low (7%). Therefore, notable changes did not occur, but significant improvements to thermal comfort could still occur in buildings that are unable to maintain high levels of thermal satisfaction.