Occupant Behaviour in Buildings: Advances and Challenges E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Serie: Frontiers in Civil Engineering
- Sprache: Englisch
Occupant behaviour in buildings is a point of interest for building designers around the world. Functional buildings have a significant energy demand; therefore, improving the thermal and energy performance of such buildings requires knowledge about the variables that influence them. However, to increase the potential for improving thermal and energy performance of buildings, studies must also consider the occupant’s interactions with the built environment. The occupant behaviour influences the conditions of the internal environment through the occupation of indoor building spaces and through the interaction with building elements, such as air-conditioning, lighting, blinds and windows. Occupant Behaviour in Buildings: Advances and Challenges brings together reviews of these influential aspects, presenting updates on advances and questions that pose challenges in our current understanding of behavioural modeling and its application to building design. Special topics covered in the book include methods to survey occupant behavior, building design choices, occupant behaviour impact on a building's thermal and energy efficiency, and,finally, a simulation of occupants in a building. Key Features- Presents up-to-date information on occupant behaviour in buildings- Eight chapters, written by renowned researchers, provide readers with useful insights on the subject- Includes a case study of buildings in Brazil- Structured reader-friendly content- References for further reading This reference is an informative resource for students and professionals in architecture, civil engineering, building information design, and urban planning. Readers interested in social and behavioural sciences will also gain insights on research methods that are helpful in investigating human behavior in urban dwellings.
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Veröffentlichungsjahr: 2021
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PREFACE
Occupant behaviour in buildings has been a matter of concern all over the world. Buildings are responsible for a significant portion of energy consumption; therefore, improving the thermal and energy performance of such buildings requires knowledge about the variables that influence them. However, to increase the potential for improving thermal and energy performance of buildings, studies must also consider the occupant’s interactions with the built environment. The occupant behaviour influences the conditions of the internal environment through the occupation of the spaces and through the interaction with building elements, such as air-conditioning, lighting, blinds and windows. Thus, the objective of this e-book is to put together some of these aspects, presenting advances and challenges, by means of eight chapters written by renowned researchers.
Due to recent technological innovations related to Information and Communication Technologies (ICTs), buildings are undergoing some evolutions and incorporating technologies that endow them with intelligence. However, the requirement of building intelligence to be related to the response of the occupants’ needs leads to the consideration of the buildings as Cyber-Physical-Social Systems (CPSS). Combining technical and social dimensions in this new generation of buildings, occupants’ satisfaction and energy use can be improved. Mateus V. Bavaresco, Ricardo F. Rupp and Enedir Ghisi concluded that by enriching data collection and presentation, more professionals can access previous outcomes and adapt their practices towards achieving comfortable and energy-efficient buildings.
People’s behaviour can significantly impact both the energy consumption and the indoor thermal environment of the buildings, and of particular interest is their window opening behaviour. A better understanding of why, when and how occupants open windows is, therefore, essential in the quest to achieve low-carbon buildings. Shen Wei provided systematic criteria for selecting a suitable monitoring method for their specific research objectives. Additionally, the author demonstrates the need for a standard method for monitoring relevant influential factors, as these varied considerably between existing studies with respect to the accuracy, interval and location. Such variation clearly has the potential to influence the ability to perform cross-study comparison.
Changing and improving the heating systems have been systematically associated with a wide range of effects, such as thermal comfort and improved air quality, which are often termed as co-benefits or ancillary benefits. Literature shows that co-benefits can be decisive when users choose a heating solution. Ricardo Barbosa and Manuela Almeida used international qualitative surveys to identify, quantify and evaluate the co-benefits associated with heating solutions, to clarify the relevance of the co-benefits in the decision-making process of building users. The results suggest that both the degree of relevance and the willingness to pay for co-benefits vary significantly amongst different national contexts.
Occupancy is a paramount factor to achieve energy efficiency. The authors António Ruano, Karol Bot and Maria Ruano, proposed a new methodology to estimate the occupancy and analysed the impacts of occupants on thermal comfort and energy efficiency in buildings from two distinct sectors: residential and educational.
The knowledge of occupant actions and needs is determinant for the proper function of an intelligent building. Therefore, the building management systems (BMS) must be supplied with data from the occupants. However the data by itself does not ensure the knowledge of occupants’ needs and the ability to predict their behaviours. To do that, BMSs must be gifted with artificial intelligence (AI) and machine learning (ML) techniques to data mine the information provided by the monitoring systems. Pedro F. Pereira and Nuno M. M. Ramos compared methodologies used to detect occupant actions and occupants’ needs in the same case study. The compared methodologies have the ability of self-learning and, therefore, can the used in multiple circumstances.
The variability of human behaviour is not taken into account in many thermal and energy performance studies, causing inconsistencies between simulation results and reality. One of the reasons for these inconsistencies also relies on adopting an opening availability schedule which is strictly limited to the occupancy schedule of a room, especially in residential buildings. Aline Schaefer, João Vitor Eccel and Enedir Ghisi studied the dependency relationship between the room’s occupancy schedule and the operation of openings in low-income houses in Florianópolis, southern Brazil. The main result has shown that the opening operation schedule often does not depend on whether the room is occupied or not and seems to rely more accordingly to a daily routine, such as the time one wakes up or goes to sleep, or leaving and coming back home.
The gap between the estimate and actual thermal and energy performance is directly and indirectly attributed to occupants. To address such issue, Arthur Santos Silva investigated the uncertainties of occupant behaviour in building performance simulation through a probabilistic approach. The author showed that the number of occupants, the schedules of occupancy of the bedrooms, the setpoint temperatures for operating the openings, the cooling setpoint of the Heating, Ventilation and Air-Conditioning system (HVAC) and the limits for operative temperatures of the rooms were the most influent variables for the thermal and energy performance, especially in the heating period. The uncertainty was up to 65.6% for estimating the degree-hours for heating (in the natural ventilation mode) and up to 59.3% for estimating the total electricity consumption with HVAC (in the hybrid ventilation mode), indicating that these operational uncertainties had a great impact on the simulation results.
Cultural heritage plays an important role in society, not only in cultural terms but also due to its touristic interest. However, it is necessary to ensure that conservation and comfort conditions are not affected, since the human body releases heat, moisture, CO2 and odours. Hugo Entradas Silva and Fernando M. A. Henriques analysed the impact of the binomial ventilation vs. occupancy, simulating various combinations of ventilation and air recirculation on the indoor air quality, conservation and energy consumption in museums. Since the visits to major national museums take usually long periods, the concept of adaptation was analysed to reduce the airflow of fresh air per visitor.
Chapter 1, written by Mateus V. Bavaresco (of the Federal University of Santa Catarina, Brazil), Ricardo F. Rupp (of Technical University of Denmark) and Enedir Ghisi (of the Federal University of Santa Catarina), explores the potentials of combining objective information gathered from technological innovations with subjective inputs obtained through qualitative methods in occupant behaviour research.
Chapter 2, written by Shen Wei, of the University College London, UK, introduces existing methods that have been used to monitor occupant window opening behaviour in buildings based on a comprehensive literature review. The author also points out relevant influential factors and discusses the advantages and disadvantages of each method.
Chapter 3 was written by Ricardo Barbosa and Manuela Almeida of the Department of Civil Engineering of the University of Minho, Portugal. The authors support the decision-making process of building users in the selection of energy-efficient heating solutions by identifying and evaluating co-benefits.
Chapter 4, written by António Ruano, Karol Bot, Maria da Graça Ruano of University of Algarve, Portugal, studied the impact of occupants in thermal comfort and energy efficiency.
Chapter 5, written by Pedro F. Pereira and Nuno M. M. Ramos of the Faculty Engineering of the University of Porto, Portugal, compared different machine learning techniques used for the detection of occupant actions in buildings and the drivers of their behaviour.
Chapter 6 was written by Aline Schaefer, João V. Eccel and Enedir Ghisi, of the Federal University of Santa Catarina, Brazil. The authors investigate the dependency relationship between the room’s occupancy schedule and the operation of openings in low-income residential buildings.
Chapter 7, written by Arthur S. Silva, of the Federal University of Mato Grosso do Sul, Brazil, investigates the uncertainties of occupant behaviour in building performance simulation through a probabilistic approach.
Hugo Entradas Silva and Fernando M. A. Henriques, of the Department of Civil Engineering, Faculty of Science and Technology, Portugal, wrote Chapter 8. The authors analyse the impact of ventilation on conservation, human health and comfort in museums.
We would like to thank all the authors who have contributed to this e-book, and the editorial team for their valuable work and completion of this e-book.
The views and opinions expressed in each chapter of this e-book are those of the authors.
List of Contributors
Exploring The Potential of Combining Technological Innovations with Qualitative Methods in Occupant Behaviour Research
Mateus V. Bavaresco1,*,Ricardo F. Rupp2,Enedir Ghisi1Abstract
The literature emphasises the important role that occupants play regarding the energy performance of buildings. Scholars have applied several methods to assess occupants’ preferences and practices in their field studies. Technological innovations such as Internet-of-Things (IoT) may capture valuable objective information that can be translated into mathematical models. Such models are vital in Building Performance Simulation (BPS) practices as they are expected to reduce performance gaps between expected and real energy use in buildings during operational phase. However, data-driven models strictly related to physical parameters exclude essential subjective information like occupant preferences and needs. There is enough evidence showing that individual differences impact on thermal preferences and levels of comfort indoors, which must also be considered in occupant behaviour studies. Aside from individual preferences, there is also social influence when occupants share spaces and the control of building systems. Several methods commonly used in social science studies are expected to incorporate the needed subjective information in this field if properly used. Therefore, this chapter explores the potentials of combining objective information gathered from technological innovations with subjective inputs obtained through qualitative methods.
INTRODUCTION
Buildings are commonly related to a high share of energy use worldwide. According to the last report by the International Energy Agency (IEA), the buildings and construction sector were responsible for 36% of the final energy use and 39% of energy and process-related CO2 emissions [1]. Therefore, huge opportunities for energy savings and reduction of CO2 emissions may be achieved by improving buildings. The energy use in buildings was the object of study of a group of 100 researchers from 15 countries, who gathered together and conducted strong research under the IEA and Energy in Buildings and Communities (EBC) Programme, in the IEA-EBC Annex 53 “Total energy use in buildings - Analysis and evaluation methods” [2]. Researchers concluded that there are six main factors that impact the energy use of buildings, i.e. climate, building envelope, building equipment, operation and maintenance, occupant behaviour, and indoor environmental conditions. As they argued, the three first are technical and physical factors, while the three last ones are human-influenced factors. Technical and physical characteristics cannot be considered as the only aspects when optimisation of energy use in buildings is intended. Indeed, technological and envelope-based interventions may reduce energy use in buildings; however, it is important to consider that they cannot guarantee this outcome alone [3]. When it comes to building operation, the literature also highlights that people in modern societies tend to spend about 85% of their time indoors [4]. It is then clear that the way occupants interact with buildings largely impact their total energy use.
Along these lines, recent research has evolved regarding the evaluation of human-related factors that impact building energy use. Considering the success of Annex 53, a different group of collaborative research was made to work on many unanswered questions. This new group, IEA-EBC Annex 66 “Definition and simulation of occupant behaviour in buildings”, was then established based on the main takeaways from Annex 53. IEA-EBC Annex 66 main objectives relied on enhancing occupant behaviour research in terms of data collection, model representation and evaluation, and integrating such models in building performance simulation practices [5]. This field presented huge improvements with the completion of Annex 66, and a large amount of work was conducted throughout the world. Then, a follow-up research group was established following the conclusion of Annex 66 since there is a need for implementing advanced occupant modelling in practical activities. IEA-EBC Annex 79 “Occupant-Centric Building Design and Operation” is developing new knowledge about occupant behaviour, focusing on applying and transferring knowledge to practitioners [6]. This new research group involves a multidisciplinary team with expertise in engineering, architecture, computer science, psychology, and sociology. Its scope encompasses the conception of guidelines, recommendations for codes and standards, the establishment of data-driven methods, as well as the creation of new occupant models and simulation tools.
A common practice in occupant behaviour research is relying on sensor-based information to objectively assess indoor conditions as well as occupant presence and actions. For instance, environmental parameters may be used to infer as well as explain occupant behaviour or presence through statistical analyses or machine learning algorithms. By inferring, we mean using such environmental parameters to deduce certain actions, e.g., by evaluating carbon dioxide concentration indoors, one may assume that a space is occupied or not [7]. When the actual occupant behaviour is also monitored, environmental parameters may be used to explain and determine boundaries for building adjustments. For instance, one may evaluate typical temperature thresholds that drive air-conditioning use [8]. In this way, the literature shows that several environmental parameters may be linked to the adjustment of building systems. Aside from occupancy, CO2 concentration was also related to window control [9-11]. Indoor [12-14] and outdoor air temperatures [12,15,16] have been related to window, blind/shade, and HVAC (Heating, Ventilation, and Air Conditioning) control, as well as adaptive actions like drinking a cold drink. Indoor humidity has been associated with thermostat adjustments [17]. Specific choices like the degrees of opening in residential windows were also related to indoor and outdoor air temperatures [18]. Solar radiation and indoor/outdoor air temperature [12,19,20] were also related to the adjustments of blinds or shades. Although several environmental parameters were already linked with occupant behaviour in buildings, there is evidence that subjective aspects also play an important role in this field.
It is evident that occupant-behaviour related studies are increasing fast in the last few years; however, more work is still necessary to properly evaluate how occupants use different building systems, as several aspects influence this role [21]. More specifically, the literature supports that multi-domain physical variables, contextual and personal factors affect both occupants’ perceptions and behaviours in buildings [22]. A huge body of research has focused on the influence of physical variables on occupant behaviour. However, there are still uncertainties related to the impact of contextual and personal factors in this field. Therefore, behavioural theories also significantly contribute to understanding personal factors and their relation with occupant behaviour, and a literature review synthesising the most commonly used theories was presented [23]. Authors acknowledged 27 approaches used in the literature, and they come mainly from the fields of psychology, sociology, and economics. Specifically, psychological theories were the most common, and the Theory of Planned Behaviour was the most frequent. Relying on the potential of applying qualitative knowledge on occupant-related research, another literature review presented methods commonly used in social sciences that are feasible to assess the human dimension of use in buildings [24]. Authors argued that broader use of qualitative methods is expected to provide building stakeholders with practical knowledge that may be helpful to achieve user-centric design and operation of buildings.
In this panorama, it is evident that both quantitative and qualitative data are vital to understand and model occupant behaviour patterns in a better way. On the one hand, technological innovations are key to collect a huge amount of quantitative data regarding building operation and objective aspects associated with the adjustments performed. On the other hand, personal and contextual variables may be missed if evaluations are solely based on objective aspects. Expertise from social sciences is necessary for this field and several methods are available. Therefore, the objective of this chapter is to assess the possibility of combining technological innovations with qualitative methods aiming to enhance occupant behaviour research practices.
INDOOR ENVIRONMENT, CLIMATE AND OCCUPANT BEHAVIOUR
The indoor environment, the climate and the occupant behaviour have an intrinsic relationship. For example, when occupants are feeling a warmer sensation, they could choose to open a window when it is cooler outside, which will decrease the indoor temperature. They could also opt to change their clothes or drink a cold beverage. Occupants could choose to turn on a fan or the cooling system, if such systems are available. The choice for any of those adaptive opportunities may vary between occupants due to their individual preferences and needs. This way, predictions of building energy consumption considering a poor representation of occupant behaviour would result in an unrealistic estimate of actual energy consumption, i.e. the so-called “performance gap” between predictions and actual energy use [25,26]. Even when certified buildings are considered, the literature supports that expected and measured energy consumptions are different [27,28]. Besides uncertainties related to climatic conditions and simulation programmes, it is evident that a better representation of occupant behaviour in simulation practices may mitigate such performance gap [29,30].
The influence of occupant behaviour on building energy use should be considered during both building design and operation. During building design, proper representation of occupant behaviour is expected to enrich the development of user-centric buildings. Additionally, computer simulation models can use a reliable representation of human-related aspects to turn actual simulation approaches more dependable. During the operation phase, understanding occupants’ preferences and behaviours are also key to maintain indoor conditions comfortable while energy is efficiently used.
As previously shown, occupant behaviour research has increased in number and importance recently. A primary aspect on this field comprises data collection; indeed, this part is not trivial, and researchers must plan their approaches carefully, considering that costs, occupant privacy, and socioeconomic factors influence it [5]. A large body of research is available on innovative sensing technologies and approaches to incorporate them into buildings to explore occupants’ behaviours, as highlighted by a literature review [31]. In fact, by collecting and processing data on this field, better modelling strategies may be achieved, as well as improvements on the indoor environmental quality of spaces. Physical monitoring relies mostly on adaptive behaviour monitoring, which includes both occupants’ actions and environmental parameters related to them [32]. Innovative approaches to collect data on occupant behaviour include, but are not limited to Building Automation Systems (BAS). Indeed, BAS may collect real-time data and provide it to building stakeholders to enhance the control algorithms of automated systems. Such an approach relies mostly on quantitative data like the occupancy of different spaces and environmental parameters that affect adjustments of building systems through their interfaces. The literature shows that some studies relied on improving occupant representation aiming to adapt the algorithms of BAS [33-35]. Other strategies are also available, as researchers should not necessarily rely on data from BAS. Individual data loggers also play an important role in data collection. Regarding residences, a literature review showed that indoor air temperature, relative humidity, and air velocity are the parameters most frequently measured in such studies [36].
In addition to those commonly measured parameters, several other aspects may also be gathered to have a deeper understanding of triggers for occupant behaviour. For instance, one of the outcomes from IEA-EBC Annex 66 is the proposition of the DNAS (Drivers, Needs, Actions, and Systems) framework to improve occupant behaviour research considering that human cognition covers a complex combination of people “inside world” (i.e. Drivers and Needs) and “outside world” (i.e. Actions and Systems) [3]. Thus, Internet-of-Things (IoT) based monitoring may benefit from several passive and active sensors available to characterise human-related influence on building operation [37]. A considerable benefit of using innovative approaches is the possibility of collecting a huge amount of data, which can be translated into boundaries for occupant-centric building design and operation.
Further improvements were reached by combining the DNAS framework with social-psychology theories to encompass subjective aspects of occupant behaviour [38]. An interdisciplinary survey was then achieved through the integration of the DNAS framework with constructs from Social Cognitive Theory (SCT) and Theory of Planned Behaviour (TPB). SCT was explained by Bandura [39]; it states that personal and environmental factors influence human behaviours, i.e.-people’s perceptions, beliefs, and acts affect their behaviours. TPB was introduced by Ajzen [40], and it evidences the impact of individual intention to behave on the behaviour itself; also, the theory supports that one’s intention to behave is influenced by attitudes, subjective norms, and perceived control towards the exercised behaviour. This interdisciplinary framework was already implemented in several office settings worldwide, and interesting conclusions were achieved [41-42].
For instance, it enabled the assessment of human-building interactions through the lenses of the Five-Factor Model (FFM) to associate occupants’ personality traits with common behavioural patterns [41]. Differences on adaptive actions undertaken by occupants to restore their thermal comfort under hot and cold discomfort, as well as conformity to social norms towards sharing the control of building systems, were shown as an indicator of energy use in offices [43]. A proposition of a broad theoretical framework to evaluate the link between indoor environmental quality and the perceived productivity of office occupants was also presented [44]. Finally, results from specific countries also added valuable information to the literature. From the Brazilian case study, the authors used the framework to conduct a theoretical-driven Structural Equation Modelling and determine the primary subjective aspects that influence occupants’ adaptive actions [45]. Results support that interventions based on social-psychology theories play an important role to boost occupants’ adaptive opportunities. The Hungarian case study added information about the importance of knowledge to control building systems – especially when complicated controls are used – which supports that training programmes may be conducted throughout the country [42]. Finally, the Italian case study synthesised all the surveyed factors in different regions of the country (north, centre, and south) to illustrate why and how knowledge from social sciences may provide valuable information to building stakeholders [42].
POTENTIALLY APPLICABLE TECHNOLOGICAL INNOVATIONS
Modern buildings can be understood as Cyber-Physical Systems (CPS) considering the advance of diverse technological innovations, which provides the opportunity to link some characteristics of the physical environment with occupants’ behaviours or preferences to reach user-centred services [46]. Cyber-Physical Systems can be understood as systems in which the physical world is combined with cyber components, and information can be exchanged between them [37]. Building Automation Systems (BAS) may benefit from these advances and can provide user-centred controls aiming to reach comfortable and energy-efficient targets for building operation. Similarly, Energy Management Systems (EMS) may become more reliable as the role of occupants is continually evaluated under these conditions; as a consequence, user-related uncertainties regarding building energy use may become less challenging.
Indeed, optimal operation is a key aspect to guarantee energy efficiency in buildings without compromising indoor environmental quality conditions. This aspect emphasises the importance of including knowledge from occupants’ preferences in building maintenance, especially considering that current building controls are mainly focused on energy-savings rather than occupants’ preferences [47]. However, the literature already supports that intelligent and autonomous controls for buildings can connect occupants with such systems by including users preferences in decision-making processes [48]. It synthesises the potential of turning the current building stock into Cyber-Physical Systems. By capturing occupants’ preferences and needs, they can be included in the loop of building control to increase indoor environmental quality while high energy efficiency levels are reached. As a consequence, building stakeholders must be aware of several opportunities provided by technological innovations in this field. By combining human preferences with up-to-date technologies, meaningful improvements may be reached through the twofold relation created. Intelligent building systems may inform occupants about the best options to control a building, e.g. opening internal blinds or shades to increase daylight penetrations and reduce artificial lighting need. However, occupants’ actions may also provide valuable knowledge that may improve the algorithms of an intelligent system, e.g. thresholds for indoor conditions may be updated when occupants adjust thermostats. Therefore, this subsection presents information about up-to-date technologies that may be used to evaluate occupants’ preferences and behaviours as well as to control building systems based on the sensed information.
Behavioural Sensing
Several up-to-date behavioural sensing technologies are available to assess the physical aspects related to buildings. Those sensors can help to understand indoor conditions, e.g. air temperature and humidity, indoor air quality, noise levels, illuminance, etc. Additionally, occupant presence and actions (OPA) can be assessed throughout them, e.g. occupancy, window opening and closing, HVAC usage, etc. The literature highlights two main groups of sensors that may be used in buildings: passive and active sensors. This topic shows the differences between these sensors and some potentials related to their use in the building sector.
Passive sensors can be characterised by their low energy use compared to active ones [49]. This is expected because passive sensors do not emit any energy to probe the space since they rely on others’ body energy. Such devices are widely used to track localisations, movements, as well as behaviours performed by building occupants. The most common passive sensor is the Passive Infrared (PIR) sensor, which is highly used to detect occupancy indoors [50]. Therefore, automated systems may be controlled according to the occupant's presence and absence; for instance, artificial lighting or HVAC systems may be turned off when no occupancy is detected in a given space. This alternative is important concerning the reduction of energy wasting during building operation. However, the actions undertaken by an automated system should be reliable to avoid bothering building occupants with the unexpected shutdown of the systems. This trend is evidenced by the literature when the control is based solely on passive sensors’ data. As these devices rely on others’ body energy, “false-off” is commonly observed in such spaces because the sensors tend to fail in detecting stationary bodies [51]. In this manner, some solutions may be considered to minimise the “false-off” issue, and the literature supports that passive sensors may be combined with other technologies to increase reliability. Indeed, capacitive sensors were presented as a solution to detect long-term stationary occupancy indoors as a promising tool to control HVAC systems [52]. A prototype using passive infrared array sensors was also created to anonymously collect occupancy data [53]. Authors concluded that such a device could detect stationary occupants, especially when lower occupancy level is detected. Passive sensors may also be combined with active ones, and the outcomes reached minimise fails in stationary detections. For instance, PIR sensors (to detect occupancy) were combined with Hall effect sensors (to detect when a door is opened or closed), and authors proposed machine-learning-based strategies to enhance occupant presence detection and activity recognition [54]. Similarly, PIR sensors were combined with plug-load meters in offices to detect energy consumption at the desk level [55]. Authors achieved high accuracies from the predictions of both presence and absence during the work (up to 99% and 96%, respectively). Therefore, it is important to highlight that even with some hindrances, low-cost approaches may be applied in buildings to improve occupant detection practices. As a consequence, reliable systems for building control may be reached throughout the building operation phase.
Passive sensors are not limited to occupancy detection in buildings, and some solutions were created to probe occupant behaviours as well. A passive wireless prototype that combines PIR with other sensors like accelerometer and environmental parameters sensors was proposed as a way to recognise activities of daily life [56]. Authors concluded that room-level resolution of activities recognition might be achieved with this non-intrusive system. Similarly, PIR sensors were combined with a piezoresistive accelerometer to develop wearable sensors and track humans’ location while also estimating their behavioural state [57]. Specific behaviours like real-time bed-egress were proposed with passive radio-frequency identification integrated with an accelerometer [58]. Passive and active sensors were combined in order to monitor specific behaviours of elderlies, and the created device was able to detect eating behaviours by estimating when items have been removed from the refrigerator and when plates have been placed in a table [59]. Some of these alternatives are highly important in households or health centres with elderly that need assistance throughout the day. However, it is important to highlight that they are not limited or exclusively valid for those situations, and all these opportunities found in the literature may be used in smart building contexts as well as in occupant behaviour studies. Indeed, the inclusion of passive sensors in low-cost solutions for the built environment may be a way to improve user-centred practices in this field. Advanced statistics, as well as machine learning approaches, are also expected to boost the usage of data from those solutions as many learning classification algorithms are currently popular.
While passive sensors rely on others’ body energy (e.g. an infrared emitting source), active sensors need internal power to operate. The literature supports that active sensors can use self-generated signals to evaluate a space as well as rely on motion to probe the intended variables [60]. On this topic, occupancy may also be detected through active sensing technologies. Occupancy estimation was proposed by evaluating indoor acoustic properties with ultrasonic chirps [61]. Such an alternative can transmit ultrasonic chirps and then assess how these signals dissipate over time to estimate indoor occupancy with algorithms. Self-generated signals and self-motion can also be combined to boost sensing capability. An example of this case can be the combination of laser range sensor and pan-tilt camera with the ability to move and detect humans even when they are positioned in blind spots – like behind other occupants [62]. Self-controlled servo-motor was also presented as an alternative to improve the detection of stationary subjects when combined with pyroelectric infrared sensors [63]. Detection of indoor localisations was proposed with stickers enabled by Bluetooth Low Energy (BLE) signals and beacons under points with the availability of Wi-Fi [64]. As argued by the authors, indoor tracking is important to improve the control of systems as well as the reliability of assistive-living services.
Another remarkable aspect of this field is that active sensors can convert one form of energy into another. Energy harvesting from environmental sources is a well-known technique, and it enables a set of green solutions like harvesting wind power through turbines. However, alternatives for doing so in micrometric scale are also available, and nanostructured piezoelectric transducers were shown as a viable solution [65]. Authors argued that this technology enables the conversion of slow fluids like human breaths into energy. Additionally to biomechanical energy harvesting, this innovation was also used to detect gait cycles [66]. Although all these alternatives still seem to fit monitoring for health care purposes, they are feasible regarding personalised monitoring, which can increase knowledge about human behaviour indoors. A positive outcome would be enhancing smart building control algorithms as well as the proposition of personalised recommendations that are expected to satisfy occupants at the same time that energy-efficient targets are maintained.
Wearable Devices
Gathering data in buildings is important for further improvements in the control algorithms or on the understanding of how the operation phase of the building impacts their energy use. Therefore, relying on the wide availability of technologies, and the improvement of sensing technologies, a set of wearable devices are also currently available. The concept of active sensors that can act like nanogenerators and harvest energy was applied to create wearable devices. The literature supports the use of triboelectric nanogenerator to create fabrics that can be used in smart clothing applications [67]. Advances in this field of intelligent clothing were presented, and the outcomes show that smart clothing can be independent of external power sources and, even so, can be integrated with other technology-driven controls [68]. Such a prototype may be used to control devices as well as monitor occupants in health centres. Another application to understand humans in health centres was based on wearable sensors to qualitatively assess arm movements in stroke survivors that need assistance [69]. Regarding the control of systems, wearable sensors with the ability to recognise human voices were also proposed [70]. Authors showed that voiceprint recognition enabled by the system was able to assess the password and the speaker, which can be used to control devices and building systems.
On the one hand, some wearable devices seem to be still in their infancy stage, as well as have theoretical applications and their use in the building sector is not common yet. On the other hand, there are pieces of evidence supporting that building stakeholders may already apply some wearable technologies to understand occupant behaviour in built environments better. For instance, wearable devices were used to understand human perceptions of an indoor environment with a view on personal attributes [71]. Such wearable devices were used to collect electrocardiogram, electrodermal, and electroencephalogram signals as physiological aspects that may be linked to occupant perception of environmental parameters, which directly influences their behaviour. Wearable sensors were also combined with stationary ones to learning occupants interactions with their workplace using machine learning algorithms [72]. An APP for Fitbit smartwatch was created to enhance the collection and labelling of comfort-related data provided by occupants [73]. This alternative is remarkable since authors enable the free download of the APP aiming a broad application throughout the world. A positive outcome reached with this approach is that instead of giving thermal comfort votes in fixed times, this wearable technology enables collection of data whenever participants want. This alternative may capture best the moments of peak discomfort indoors to tailor environmental conditions to meet occupants’ requirements. Advances within this study were recently published, and other dimensions of indoor environmental quality were included (visual and acoustic) [74]. Authors argue that humans can act as sensors within these conditions, and important improvements in this field may be reached.
Internet-of-Things
The literature shows that by monitoring the operation of buildings as much as possible, the comprehension of building performance based on data-driven outcomes will improve [37]. Authors argued that data-driven decision-making may guide the proper discovery of both user-related adversity and system malfunctions. Solutions for those problems may be set by building stakeholders with the wider availability of information. Relying on the availability of several sensing technologies, a relevant fact to turn current buildings into Cyber-Physical Systems, as previously mentioned, is with the concept of Internet-of-Things (IoT). In fact, IoT-based solutions can connect several objects (physical) with cyber components that provide opportunities to better understand their relationship. Such an alternative is meaningful for the building sector because it can be embedded in automation systems to enable smart control of devices (e.g. smart lighting [75]). It is worth mentioning that those alternatives may represent high costs, which is an evident hinder for its application, especially in developing countries. However, the concept of IoT may be reached in several creative manners. The literature supports the use of everyday objects like smartphones to evaluate the patterns of HVAC usage in households [76]. Smartphones were also used to sense the magnetic field inside buildings, which was used as a proxy to determine occupant localisation indoors [77]. Such approaches are feasible considering that smartphones present several built-in sensors, which can probe intended variables and be included in the loop of an IoT-based system. In a broader perspective, a recent literature review highlighted that the advance of IoT and wireless networks empowered a quick increase in occupant-centric urban data [78].
Components of an IoT system also provide valuable information that can be used in the building-control loop. For instance, device-free occupancy detection and counting were proposed by evaluating the channel state information (CSI) observed in IoT systems [79]. Authors used the propagation signals of Wi-Fi as a proxy to identify whether there are occupants, as well as count them, in a built environment. Similarly, based on channel state information and advanced machine learning algorithms, it is possible to infer occupant activity inside buildings [80]. Such approaches highlight the wide possibilities provided by IoT-based systems. Indeed, the propagation of Wi-Fi signals from transmitters to receivers’ components encapsulates meaningful information, as physical components like walls, doors, furniture, and occupants impact the way such signals are propagated. By recognising the impact of fixed elements on the signals, machine-learning-based approaches can infer the extent that occupants’ presence and actions influence this characteristic. Besides that, IoT-based systems are also useful for real-time monitoring of indoor conditions, which is expected to increase the knowledge about healthy and comfortable built environments. Indoor air quality monitoring was proposed with a low-cost IoT-based tool that relies on measurements of environmental parameters (air quality, temperature, and humidity) coupled with a Raspberry Pi microprocessor and cloud storage [81]. Additionally, a literature review regarding technologies and practices regarding occupant-centric thermal comfort in buildings highlighted that the advances in IoT enable the vast collection of occupants’ responses, which is promising to include occupants’ feedback into building control [82]. IoT frameworks are handy in this case and have been used to improve the communication between users and HVAC systems to enable user-centric control [83].
In addition to occupant votes being used to tailor HVAC operation in a user-centric manner, such information is also important as a way to understand occupant preferences and behaviours. The role of feedback is presented in the literature as an important alternative in IoT-based systems [84]. Concerning this matter, it is important to understand that in addition to occupant preferences being a source of knowledge to adapt systems control, the existing system may provide some feedback to occupants. By means of integrated platforms, occupants may adjust their actions in order to save energy without compromising their preferences regarding indoor environmental quality (IEQ). With this broad applicability, the literature emphasised a clear path to deliver buildings with high IEQ levels and low energy use. For instance, with real-time monitoring of indoor conditions, feedback and behavioural-based consumption change may be achieved, which is a key to enhance energy management systems in buildings continually [37].
Virtual Reality
Virtual reality and immersive environments are emerging as promising tools to improve occupant behaviour research. These technologies enable longitudinal studies regarding occupants’ preferences and behaviours in short-term experiments [85]. A wide variability of conditions can be tested under virtual representations instead of real-world experiences. This alternative may reduce the costs of a given experiment as smaller time frames are enough to drive conclusions [86]. The literature emphasised that virtual environments were used to collect occupant-related information like lighting preferences [87], internal blinds’ adjustments [86], as well as people movements [88]. Although promising, this field still needs more evidence to support that outcomes reached within virtual environments actually correspond to real-world sensations [89]. Additionally, the literature supports that some people may face cyber or motion sickness when experience some virtual environment [89,90]. Researchers must be aware of this issue and guarantee some practices that are expected to reduce discomfort of those participants. For instance, the literature supports the use of a Simulator Sickness Questionnaire to detect the sickness tendency of different individuals previously to the realisation of the experiment [90]. Occupants with different tendencies of cyber sickness can be therefore selected for pilot studies of the designed experiment to adjust the practice before a higher sample is reached. Additionally, limiting the time of each experiment is also a potential alternative to reduce cyber sickness, as long exposition to virtual environments may be disliked by participants.
QUALITATIVE METHODS TO STUDY OCCUPANT BEHAVIOUR
Driving factors for occupant behaviour in buildings are largely discussed in the literature, but standardised methods for assessing them are still lacking [91]. On the one hand, this might be interpreted as a weakness of this study area because results obtained with different experiments may be hardly compared. On the other hand, such a lack of standardised approaches provides practitioners with several opportunities for tailoring occupant behaviour evaluations to current needs as well as available resources. The literature already supports that understanding subjective aspects that lead to energy use in buildings is crucial, as focusing specifically on physical parameters may not be enough [92]. In fact, occupant behaviour research needs multidisciplinary efforts [30], highlighting the importance of adding qualitative data to those studies. Although some subjective or contextual factors may not enhance the mathematical representation of building performance, they are expected to provide practical advice through case studies for improvements in building design and operation [93]. As emphasised by Sovacool [94], energy studies need social science approaches, and this combination can make energy research practices more socially oriented, interdisciplinary and heterogeneous. As a consequence of mixing qualitative and quantitative data, analytic excellence and social impact are more likely to be achieved.
Questionnaires and interviews are the most commonly used approaches when subjective evaluations are included in occupant behaviour research, as shown in a book on occupant behaviour studies [31]. The book presented a chapter about the qualitative methods mentioned (questionnaires and interviews) and provided important information about the state-of-the-art and future steps. Literature reviews focusing specifically on the use of questionnaires in this field were also published [95,96]. With advances in this area, energy efficiency studies are becoming more user-centric and several underlying effects that may be explained by behavioural theories are being incorporated in research practices as highlighted by a recent literature review [23]. Therefore, considering the importance of assessing subjective aspects in occupant behaviour research, several potential methods used in social science studies are available [24,97]. In this topic, some qualitative methods are presented as a promising way to include subjective data on occupant behaviour evaluation.
Questionnaires
Questionnaires are highly used in energy-related research, and previous literature reviews have presented an in-depth evaluation of approaches and challenges related to them. Considering residential contexts, Carpino et al. [95] concluded that, although the use of this method has increased recently, the use of non-standardised nomenclature may represent a source of uncertainty. Authors then provided some guidance for future research, considering the need to present detailed information about the sample evaluated, as well as the homogenisation of the nomenclature used. Another literature review focused on the use of cross-sectional questionnaires in occupant behaviour research [96]. In this case, the authors concluded that the projects reviewed were mostly focused on environmental and engineering factors. Therefore, they suggested that future research should encompass multidisciplinary approaches to gather more representative knowledge from field studies. Another literature review provided comprehensive information about different approaches and specific features when questionnaires are applied [24]. Authors reported common types and scales used, as well as the types of questions frequently employed.
Regarding the types of questionnaires, right-here-right-now, cross-sectional, and longitudinal ones are commonly used in the field [24]. Right-here-right-now questionnaires comprise the approach of asking subjects for their right-in-time opinions, perceptions or behaviours. As the questions are focused on the current moment, retrospective biases are more likely avoided when compared to approaches in which the respondent must provide information about past events. Right-here-right-now questionnaires are a standard procedure to collect occupants’ perceptions about the thermal environment, and many researchers rely on the ASHRAE 55 method [98]. Besides its frequent use in thermal comfort studies, such an approach may be applied for other purposes as well. For instance, different dimensions of indoor environmental quality (e.g. lighting, air quality, and acoustics) may be assessed through right-here-right-now questionnaires. If combined with concurrent objective measurement, they can provide meaningful information to understand multi-modal comfort aspects better. Cross-sectional questionnaires comprise approaches in which larger timeframes are evaluated [99], and they enable understanding tendencies on the topic of interest. As previously shown, this approach is common in occupant behaviour research [96]. Different from right-in-time questions, this approach may cause some confusion in the respondents when a large timeframe is comprised. It is recommended to limit the scope of the questions to the current season or month instead of asking about whole-year experiences of occupants [24]. The literature supports the use of this approach to assess patterns of occupant behaviours [100-102], as well as constructs related to it [103-105]. Finally, longitudinal questionnaires represent the scenario in which participants’ opinions are asked more than once in a given period. Both right-here-right-now and cross-sectional methods can be combined in a large-scale longitudinal evaluation [24]. This approach can provide practitioners with valuable information about differences in occupants' perceptions between seasons [106] or after interventions on the building [107]. An important aspect of this kind of research is the frequency of questionnaire applications since intensive data collection may bother participants and do not result in high-quality outcomes.
The type of questions used in survey-based studies is another important aspect. Commonly, both close-ended and open-ended questions are applied in energy research. Close-ended questions are those in which pre-defined options are presented to participants, and they are asked to either choose one (mutually exclusive) or to select all options that apply (collectively exhaustive). In practical terms, mutually exclusive questions can facilitate the comparison among trends reported; however, only one option must apply to avoid frustration during the participation [97]. A common approach to guarantee this is the use of Likert questions, which relies on asking participants to what extent they agree or disagree with a given statement. Similarly, Likert-like questions are highly used in this field to ask varied opinions of participants regarding the topic of study – not necessarily if they agree or not. For instance, the literature emphasises the use of Likert-like questions to assess occupants satisfaction with varied aspects of indoor environmental quality [108-110]. One key aspect when using this approach is guaranteeing symmetry on the options regarding agreement and disagreement [97]. Collectively exhaustive questions – also known as check-all-that-apply questions – provide to participants the option to select as many options as they want. Importantly, both literature review and pilot studies are needed to create those close-ended questions, because options given must be related to the participants’ experiences; otherwise, inconclusive responses may be obtained. Finally, open-ended questions enable the participants to provide their opinion. It requires more cognitive efforts from them, but important and unexpected information may be obtained in the evaluation. Besides the possibility of one question (or even the whole questionnaire) be open-ended in a study, an open-ended feature can be included in a close-ended question by providing the option “Other” and allowing participants to write an explanation about this difference [24].
Interviews
Interviews are also highly used in occupant-behaviour-related research, and both individual or in-group data collection may be considered [31]. Besides the different approaches used, interviews can be compared to questionnaires as a set of questions are asked to participants; therefore, data collection can also be structured or open-ended. Fully-structured interviews are those in which all the participants respond to a set of pre-defined questions. This approach may be helpful when specific aspects need to be evaluated [111]. Semi-structured interviews are not completely pre-defined, but a previous structure is created as well. This method allows for adding topics that emerged during the discussions in the evaluation. Finally, open-ended interviews allow participants to explain their opinions about a few pre-established points of interest. Fully-structured interviews enable the collection of structured data, which may facilitate the comparison of all the responses. On the other hand, open-ended questions may facilitate the discussion between the interviewer and the participants [111], as well as enable the collection of powerful stories [112]. Collecting stories is shown as a valuable way to inform building stakeholders about malfunctions as well as opportunities to improve building performance, especially considering that stories can be more easily remembered comparing to objective outcomes like numbers [112].
Individual interviews can be conducted either face-to-face or remotely. Both approaches are valid, and stakeholders should determine which method suits best their research interest. For instance, face-to-face interviews are expected to be more time- and resource-consuming, especially when conducted in situ, compared to an interview viatelephone or video. However, the literature highlights that in situ interviews may provide some underlying information or aspects that can be observed instead of asked [112]. In some cases, the interviews must be performed in situ. For instance, asking questions to specific occupants like children in kindergartens requires an in situ interaction between the child and the interviewer in order to allow the child to become familiar with the researcher before conducting the interview [113,114]. Additionally, interviewers can assess directly with occupants some daily practices regarding building control, e.g. dwellers can describe and re-enact common practices that lead to energy use in their households [115
