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To improve occupant comfort and save energy in buildings, we have developed a closed-loop air conditioning (AC) sensor-controller that predicts occupant thermal sensation from the thermographic measurement of skin temperature distribution, then uses this information to reduce overcooling (cooling-energy overuse that discomforts occupants) by regulating AC output. Taking measures to protect privacy, it combines thermal-infrared (TIR) and color (visible spectrum) cameras with machine vision to measure the skin-surface temperature profile. Since the human thermoregulation system uses skin blood flow to maintain thermoneutrality, the distribution of skin temperature can be used to predict warm, neutral, and cool thermal states. We conducted a series of human-subject thermal-sensation trials in cold-to-hot environments, measuring skin temperatures and recording thermal sensation votes. We then trained random-forest classification machine-learning models (classifiers) to estimate thermal sensation from skin temperatures or skin-temperature differences. The estimated thermal sensation was input to a proportional integral (PI) control algorithm for the AC, targeting a sensation level between neutral and warm. Our sensor-controller includes a sensor assembly, server software, and client software. The server software orients the cameras and transmits images to the client software, which in turn assesses occupant skin temperature distribution, estimates occupant thermal sensation, and controls AC operation. A demonstration conducted in a conference room in an office building near Houston, TX showed that our system reduced overcooling, decreasing AC load by 42% when the room was occupied while improving occupant comfort (fraction of “comfortable” votes) by 15 percentage points.

Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

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.

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs.

In buildings, one or a combination of systems (e.g., central HVAC system, ceiling fan, desk fan, personal heater, and foot warmer) are often responsible for providing thermal comfort to the occupants. While thermal comfort has been shown to differ from person to person and vary over time, these systems are often operated based on prefixed setpoints and schedule of operations or at the request/routine of each individual. This leads to occupants’ discomfort and energy wastes. To enable the improvements in both comfort and energy efficiency autonomously, in this paper, we describe the necessity of an integrated system of sensors (e.g., wearable sensors/infrared sensors), infrastructure for enabling system interoperability, learning and control algorithms, and actuators (e.g., HVAC system setpoints, ceiling fans) to work under a governing central intelligent system. To assist readers with little to no exposure to artificial intelligence (AI), we describe the fundamentals of an intelligent entity (rational agent) and components of its problem-solving process (i.e., search algorithms, logic inference, and machine learning) and provide examples from the literature. We then discuss the current application of intelligent personal thermal comfort systems in buildings based on a comprehensive review of the literature. We finally describe future directions for enabling application of fully automated systems to provide comfort in an efficient manner. It is apparent that improvements in all aspects of an intelligent system are be needed to better ascertain the correct combination of systems to activate and for how long to increase the overall efficiency of the system and improve comfort.

Inhalation exposure to pure and metabolic elevated carbon dioxide (CO2) concentration has been associated with impaired work performance, lower perceived air quality, and increased health symptoms. In this study, the concentration of metabolic CO2 was continuously measured in the inhalation zone of 41 subjects performing simulated office work. The measurements took place in an environmental chamber with well-controlled mechanical ventilation arranged as an office environment. The results showed the existence of a personal CO2 cloud in the inhalation zone of all test subjects, characterized by the excess of metabolic CO2 beyond the room background levels. For seated occupants, the median CO2 inhalation zone concentration levels were between 200 and 500 ppm above the background, and the third quartile up to 800 ppm above the background. Each study subject had distinct magnitude of the personal CO2 cloud owing to differences in metabolic CO2 generation, posture, nose geometry, and breathing pattern. A small desktop oscillating fan proved to be suitable for dispersing much of the personal CO2 cloud, thus reducing the inhalation zone concentration to background level. The results suggest that background measurements cannot capture the significant personal CO2 cloud effect in human microclimate.

The ability to inexpensively monitor indoor air speed and direction on a continuous basis would transform the control of environmental quality and energy use in buildings. Air motion transports energy, ventilation air, and pollutants around building interiors and their occupants, and measured feedback about it could be used in numerous ways to improve building operation. However indoor air movement is rarely monitored because of the expense and fragility of sensors. This paper describes a unique anemometer developed by the authors, that measures 3-dimensional air velocity for indoor environmental applications, leveraging new microelectromechanical systems (MEMS) technology for ultrasonic range-finding. The anemometer uses a tetrahedral arrangement of four transceivers, the smallest number able to capture a 3-dimensional flow, that provides greater measurement redundancy than in existing anemometry. We describe the theory, hardware, and software of the anemometer, including algorithms that detect and eliminate shielding errors caused by the wakes from anemometer support struts. The anemometer has a resolution and starting threshold of 0.01 m/s, an absolute air speed error of 0.05 m/s at a given orientation with minimal filtering, 3.1° angle- and 0.11 m/s velocity errors over 360° azimuthal rotation, and 3.5° angle- and 0.07 m/s velocity errors over 135° vertical declination. It includes radio connection to internet and is able to operate standalone for multiple years on a standard battery. The anemometer also measures temperature and has a compass and tilt sensor so that flow direction is globally referenced regardless of anemometer orientation. The retail cost of parts is $100 USD, and all parts snap together for ease of assembly.

‘Personal comfort systems’ and thermally active clothing are able to warm and cool individual building occupants by transferring heat directly to and from their body surfaces. Such systems would ideally target local body surfaces with high temperature sensitivities. Such sensitivities have not been quantified in detail before. Here we report local thermal sensations and sensitiv-ities for 318 local skin spots distributed over one side of the body, measured on a large num-ber of subjects. Skin temperature changes were induced with a thermal probe 14 mm in diame-ter, and subjective thermal sensations were surveyed after 10 seconds. Our neutral base tem-perature was 31oC and the spot stimulus was ±5oC. Cool and warm sensitivities are seen to vary widely by body part. The foot, lower leg and upper chest are much less sensitive than average; in comparison, the cheek, neck back, and seat area are 2-3 times as sensitive to both cooling and warming stimuli. Every body part exhibits stronger sensitivity to cooling (1.3–1.6 times stronger) than to warming. Inter-personal differences and regional variance within body parts were observed to be 2-3 times greater than potential sex differences. These high-density thermal sensitivity maps with appended dataset provide the most comprehensive distributions of cold and warm sensitivity across the human body.

Energy consumption in office buildings highly depends on occupant energy-use behaviors and intervening these behaviors could function as a cost-effective approach to enhance energy savings. Current behavior-intervention techniques extensively rely on occupant-specific energy-use information at the workstation level and often ignore shared appliances. It is because an occupant typically has full responsibility for her workstation appliances energy consumption and shares the responsibility of the shared appliances energy consumption. However, understanding energy-use behavior of both workstation and shared appliances is necessary for applying appropriate behavior-intervention techniques. Despite this importance, there is still no practical and scalable method to capture personalized energy-use information of workstation and shared appliances since the conventional methods use plug-in power meters that are extremely expensive and difficult to maintain over long period of time. To address this gap, we propose a comprehensive occupant-level energy-usage approach which utilizes the data from the internet of things devices in office buildings to provide information related to energy-use behavior of workstation and shared appliances of each occupant in an economical and feasible manner. In particular, we introduce an energy behavior index which quantitatively compares individual occupants’ energy-consuming data to identify high energy consumers and inefficient behaviors. Results from an experiment conducted in an office building equipped with internet of things devices demonstrate the feasibility of the proposed approach to classify occupants to different energy-usage categories. Our proposed approach along with appropriate behavior-intervention techniques could be used to impact occupant energy-use behaviors.

Wi-Fi networks are currently considered as an efficient and economical tool for occupancy sensing in office buildings. Studies particularly indicated that these networks could be utilized to understand/predict occupants’ energy-use patterns. Despite the value that investigating this possibility could provide for the current research, it has not been well explored how energy-use pattern information could be extracted from Wi-Fi system information. In response, this study utilizes statistical analyses to investigate the correlation of Wi-Fi flows with miscellaneous electric loads (MELs) in office buildings. MELs account for more than one-third of office-building energy consumption and are the best representative of occupants’ energy-use patterns. In the pursuit of the objective, data from two offices were collected over a 3-month period of time. Results from the analyses show that an average 92 percent of MELs energy consumption could be predicted through the Wi-Fi flows in a building. This finding thereby demonstrates that occupants’ energy-use patterns are highly positively correlated to Wi-Fi flows in a building and accordingly, the information of Wi-Fi networks could be utilized to understand/interpret these patterns. This significantly contributes to the current body of research and can be used to support efforts into understanding/enhancing occupants’ energy-use behaviors. In addition, since Wi-Fi networks are a major subset of internet of things (IoT) hardware systems and IoT implementation for intelligent energy management in buildings significantly depends on occupant energy-use patterns, this research helps IoT-based efforts by displaying how these patterns could be extracted from IoT infrastructure.

Indoor air movement affects many functions of buildings, including ventilation and air quality, comfort and health of occupants, fire safety, and building energy use. Accurately measuring air movement has been difficult and expensive over extended periods of time, especially for velocities below 1 m/s. A new type of high frequency ultrasonic transceiver provides high sensitivity measurements and low cost through microelectromechanical systems (MEMS) manufacturing. However, at high frequencies, conventional ultrasonic signal processing algorithms function only over small ranges of ambient temperature and velocity. In this paper, we describe three algorithms that use the complex phase angle of an ultrasonic pulse to measure velocity and temperature over extended ranges of temperature and velocity. They employ heuristics to track the vibration cycle of the measured phase angle. These methods are applied in a pulse-based anemometer whose 176kHz MEMS transceivers both transmit and receive. In wind tunnel tests between 0-4 m/s, the tracking algorithm with a low-pass filter measured air speed with high sensitivity and accuracy (0.026 m/s mean absolute error). The ability to monitor to this accuracy with such low power draw and low cost is currently unprecedented in the industry.

High CO2 concentration in inhaled air has been shown to negatively impact work performance and increase acute health symptoms. As respiratory CO2 is constantly exhaled, it may not dissipate in surrounding air in absence of adequate air movement and is instead re-inhaled into the airways (breathing in a CO2-rich bubble). In this study, we explored the impacts of context-dependent factors such as office activities, desk settings, and personal differences on the inhalation zone CO2 concentration and on concentrations at a below-neck wearable sensor. While all factors were found to significantly impact measurements at both measuring points, desk settings (empty desk, desk with a fan, desk with laptop, desk with monitor) was found to be the most dominant factor. Presence of a small portable desk fan was found to significantly reduce the CO2 concentration. On average, we observed a 177 ppm reduction in CO2 concentration when using a fan, which is 25 ppm higher than the background CO2 measurement (650 ppm). Among 41 test subjects, we found distinct relationships between the inhalation zone CO2 concentration and the wearable sensor measurements and, by applying a hierarchical clustering algorithm, we found 4 clusters of relationships. While below-neck wearable sensors could be used as an exact measure of inhalation of CO2 concentration for 29% of the subjects, we identified a boundary point (917 ppm) separating high and low inhalation zone CO2 concentration measurements.

Personal comfort systems (PCS) have potential to fulfill building occupants' personal thermal comfort preferences with great efficiency. But to integrate them into building conditioning, there must be a broader selection of PCS devices available. Design guidance and standards are needed to assure that such devices provide high levels of comfort effectiveness and energy efficiency. This study addresses these needs. A suite of minimum-power PCS devices was built that target body parts significant to alliesthesia—a heated shoe insole, heated/cooled wristpad, small deskfan, and heated/cooled chair. They were tested in a climate chamber under cool and warm conditions using both thermal-manikin and human-subjects. Their efficiency at physically heating/cooling the body is high; the combined suite has a coefficient of performance (COP) of 3.6 for cooling and 0.88 for heating. The subjects' whole-body thermal acceptance and thermal comfort perception were improved by the devices in an additive manner; using the combined suite over 80% of people accepted ambient temperatures of 18°C and 29°C. The PCS ‘corrects’ the ambient temperature towards thermal neutrality by as much as 6.5 K cooling and 3.6 K heating, overcoming building occupants' typical interpersonal thermal differences and making possible large HVAC energy savings in buildings. The idea of temperature corrective power can be the basis of standards for PCS.

We introduce a systematic approach for analyzing the energy consumption of four control policies (i.e., zone level daily optimal control, zone level annual optimal control, building level daily optimal control, building level annual optimal control), which differed based on their temporal and spatial control scales. In order to integrate occupant thermal comfort requirements, we defined uniformly distributed random constraint functions on the setpoints. We used the DOE reference small office building in three U.S. climate zones for simulating the performances of control policies, using EnergyPlus. Among the four control policies, the building level annual control policy showed close to the highest energy efficiency (27.76% to 50.91% (average of 39.81%) savings depending on the climate) with comparatively small training data requirements. In addition, the building level daily optimal setpoint selection, subject to thermal comfort constraints, leads to 17.64 – 38.37% (average of 26.61%) energy savings depending on the climate. We also demonstrate that temporal scale of the policies have a statistically significant impact on the small office building’s energy consumption while spatial scale’s impact is insignificant.

Personal comfort systems (PCS) aim to efficiently fulfill building occupants’ personal thermal comfort demands, but to date not many have been manufactured and evaluated. Based on the observation that foot/hand warming are most effective in cool conditions, and head cooling is most effective in warm environments, we built and tested a suite of PCS devices--heated in-sole, heated/cooled wristpad, small deskfan, heated/cooled chair--and evaluated the thermal effect of each device and of combinations of the four. Human-subject and thermal-manikin tests in a climate chamber under cool and warm conditions (18oC and 29oC) investigated the thermal comfort improvement and heating/cooling performance of these devices. The results show the devices to have remarkable heating/cooling efficiencies, with combined cooling COP of 3.6 and heating COP of 0.88. They significantly improved subjects’ whole body thermal acceptance and thermal comfort perception, with more than 80% of people accepting the test-ed ambient temperatures. The application of these PCS devices can correct up to 2.6K heating and 4.2K cooling of the ambient temperature towards neutral which may contribute to re-markable HVAC energy savings in buildings.

Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a four-day long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

Previous research efforts for optimizing energy usage of HVAC systems require either mathematical models of HVAC systems to be built or they require substantial historical operational data for learning the optimal operational settings. We introduce a model-free control policy that begins learning optimal settings with no prior historical data and optimizes HVAC operations. The control policy is an adaptive hybrid metaheuristic that uses real-time data, stored in building automation systems (e.g., gas/electricity consumption, weather, and occupancy), to find optimal setpoints at the building level and controls the setpoints accordingly. The algorithm consists of metaheuristic (k-nearest neighbor stochastic hill climbing), machine learning (regression decision tree), and self-tuning (recursive brute-force search) components. The control policy uses smart selection of daily setpoints as the control basis, making the control schema complementary to legacy building management systems. To evaluate, we used the DOE reference small office building in all U.S. climate zones and simulated different control policies using EnergyPlus. The proposed algorithm resulted in 31.17% energy savings compared to the baseline operations (22.5 °C and 3 K). The algorithm has a superior performance in all climate zones for the goodness of measure (i.e., normalized root mean square error) with a value of 0.047.

There are several spaces in cities that are never used, although citizens are allowed to be in them. It seems that these spaces are invisible and do not exist. The existence of these spaces can be caused by various factors, such as weakness in planning and design, or functional and physical limitations. By ignoring them, social and safety issues will arise over time and also they can turn into a weakness for the city. This is despite the fact that many of these spaces have the potential to turn into quality places. This article which is a type of field research, describes the experience of Hamsaakht Urban Activists Society-NGO in revitalizing an urban void adjacent to Sobhani Square in Shiraz, Iran to find how activists and non-governmental organizations can be effective in revitalizing urban voids, with the participation of users, and what challenges do they face in this way? And what effects will the revitalization of this urban void have on other similar spaces in the neighborhood? The current research methodology involves field research using the observation technique of sample members. Activists encourage residents to participate and develop a sensitivity towards the space, to improve their sense of belonging. The aim is to determine whether meaningful physical changes will occur in this place compared to other similar spaces in the Maba'as residential complex and what effects these changes will have on other spaces. The research results indicate that despite the success of tactical urbanism methods, users were hesitant to actively participate in costs and social capital was inadequate due to a lack of trust in city authorities and a generalization to other urban activists. Additionally, a weak sense of belonging was observed due to rental and instable accommodation. However, the project had a positive impact on the municipality and board members of the Maba'as residential complex, and steps have been taken to revitalize similar urban voids. [ABSTRACT FROM AUTHOR]

The common practice of defining operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings is to use fixed set points, which assume occupants have same and static comfort requirements. However, thermal comfort varies from person to person and also changes due to climatic variations or acclimation, making it dynamic. In addition, thermal comfort in transient conditions are different from the steady state conditions, which makes the prediction of thermal comfort more difficult. Thus, thermal comfort has to be monitored over time. In this paper, we present a novel infrared thermography based technique to monitor an individual’s thermoregulation performance and thermal comfort levels by measuring the skin temperature on several points on human face, which has a high density of blood vessels and is not usually covered by clothing. Unlike other methods, our method requires no continuous user input or interaction. Our results demonstrate that the monitored facial points behave differently under the heat and cold stresses and it can be explained based on the underlying vascular territories. We define two heuristics to describe the thermoneutral zone based on the observed behaviors and estimate thermal comfort for individuals with 95% confidence level. Considerable variations are observed in the thermoregulation performance and uncomfortably cool conditions metrics between the males and females. Females’ thermoregulation system responses are less sensitive to the perception of warm conditions. However, similar behaviors are observed for uncomfortably cool conditions across genders.

This paper provides a systematic approach for quantifying the influence of building size, construction category, climate, occupancy schedule, setpoint, and deadband on HVAC energy consumption in office buildings. Simulating the DOE reference office buildings of three sizes and three construction categories in all United States climate zones, using the EnergyPlus, we conducted several N-way ANOVA analyses to study the interrelationships between setpoints, deadbands and several building related and environment related factors. In summary, daily optimal deadband selection of 0, 1, 2, 4, 5, and 6 K would result in an average energy savings of -70.0, -34.9, -13.7, 9.6, 16.4, and 21.2 %, respectively, compared to baseline deadline of 3 K. Selecting the daily optimal setpoint in the range of 22.5 ± 1 °C, 22.5 ± 2 °C, and 22.5 ± 3 °C would result in an average savings of 7.5, 12.7, and 16.4 %, respectively, compared to the baseline setpoint of 22.5 °C. Additionally, we found that when the outdoor temperature is within -20 to 30°C, the optimal setpoint depends on the building size. We also observed a range of outdoor temperatures (e.g., 9 – 14 °C for small buildings and 8 – 11 °C for medium buildings) where the setpoint selection would only slightly influence the energy consumption. However, the choice of setpoints becomes very influential (up to 30% of energy savings) where the outdoor temperatures are slightly outside the mentioned ranges on either direction. The potential savings from selecting daily optimal setpoints in the range of 22.5 ± 3 °C in different climates and for small, medium and large office buildings, would lead to 10.09 – 37.03%, 11.43 – 21.01%, and 6.78 – 11.34 % savings, respectively, depending on the climate.

HVAC systems are the major energy consumers in commercial buildings in the United States. These systems are operated to provide comfortable thermal conditions for building occupants. The common practice of defining operational settings for HVAC systems is to use fixed set points, which assume occupants have static comfort requirements. However, thermal comfort has been shown to vary from person to person and also change over time due to climatic variations or acclimation. In this paper, we introduce an online learning approach for modeling and quantifying personalized thermal comfort. In this approach, we fit a probability distribution to each comfort condition (i.e., uncomfortably warm, comfortable, and uncomfortably cool) data set and define the overall comfort of an individual through combing these distributions in a Bayesian network. In order to identify comfort variations over time, Kolmogorov–Smirnov test is used on the joint probability distributions. In order to identify comfortable environmental conditions, a Bayesian optimal classifier is trained using online learning. In order to validate the approach, we collected data from 33 subjects, and an average accuracy of 70.14% and specificity of 76.74% were achieved. In practice, this approach could transform the comfort objectives to constrain functions and prevents pareto optimality problems.

With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption of the highest or lowest setpoint or setback to minimize energy consumption in hot or cold climates, respectively. However, at outdoor temperatures where variations in occupancy heat loads prompt buildings to fluctuate across cooling, free-running, and heating mode, optimal setpoints and setbacks are not always the lowest or highest. In addition, the perturbations caused by rapid switching between setpoint and setback could diminish energy savings due to system destabilization. This paper aims to systematically compare the potential energy savings from fixed and optimal setpoints and setbacks across wide-ranging occupancy scenarios (four occupancy rates and 14 patterns). Energy simulations were conducted using the Department of Energy reference models for small, medium, and large office buildings to enable an exhaustive search of optimal setpoint/setbacks in 17 climate zones. Explored setpoints were 19.5 °C to 25.5 °C with intervals of 1 °C, and setbacks were 17 °C/19 °C for heating and 26 °C/28 °C for cooling. The findings indicate that, on average, while lower occupancy heat loads results in 5.48% energy reduction, a conventional fixed setpoint and setback strategy provides an additional 11.80%, and optimal selection of setpoints and setbacks could provide an additional 34.36–38.08%, emphasizing the untapped potential energy saving. To facilitate practical applications, this paper presents an interactive graphical interface: Optimal Temperature Setpoint Tool. [ABSTRACT FROM AUTHOR]

Background: Flipped or reverse class model is a new educational method which created a great revolution in education, significantly higher education, based on new technologies. The flipped class is based on the reliable theoretical foundations and represents a unique combination of learning theories. Team-based and collaborative learning is very inclusive and uses peer evaluation and immediate feedback to ensure individual and team responsibility to promote the learning outcomes. This study aims to provide a scoping of research studies conducted connected to flipped classroom based on team-based and collaborative learning. Methods: This study used a scoping review method to identify, evaluate, and analyze studies on flipped classrooms based on team-based and participatory learning. The study population is the articles related to flipped classroom, team-based learning, and participative learning in higher education from 2012 to 2022. The articles in this review were selected with criteria including articles with features such as Persian or English publishing language. Then, by quantitative or qualitative approaches, done during the last 10 years. Furthermore, the keywords were flipped classroom, collaborative learning, team learning, team-based learning, and higher education. Results: The findings show a positive effect of flipped classrooms on team-based and participative learning. The flipped classroom positively affects peer interaction with teachers, enhances collaboration, strengthens team building, and enhances teamwork. Conclusion: When using a participatory strategy in a flipped classroom, team building, and teamwork strategies could be considered if we look for significant motivational achievements for students. Educators should consider combining the participatory process with active learning methods such as the flipped classroom approach when using the participatory process. [ABSTRACT FROM AUTHOR]