Towards user centered building design: Identifying end-user lighting preferences via immersive virtual environments

Automation in Construction 81 (2017) 56–66

Contents lists available at ScienceDirect

Automation in Construction journal homepage: www.elsevier.com/locate/autcon

Towards user centered building design: Identifying end-user lighting preferences via immersive virtual environments

MARK

Arsalan Heydariana, Evangelos Pantazisa, Alan Wangb, David Gerberc,⁎, Burcin Becerik-Gerbera a b c

Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA, USA School of Architecture, University of Southern California, Los Angeles, CA, USA School of Architecture, Department of Civil and Environmental Engineering (Courtesy Joint), University of Southern California, Los Angeles, CA, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Lighting preference User-centered design Immersive virtual environment Design decision making

In this paper, a systematic approach is presented to (1) collect end-user lighting-related behavior by using immersive virtual environments (IVEs) as an experimental tool, (2) integrate the collected data with building performance simulation (BPS) tools in order to translate behavioral information into quantitative measures (i.e., preferred lux level), and (3) incorporate user preference data for evaluating design alternatives with the objective of meeting end-user lighting preferences while reducing the building lighting-related energy consumption. To evaluate the applicability of this approach, 89 participants' lighting preferences, performance (reading speed and comprehension), personality traits, and environmental views were collected in IVEs. BPS tools were used to translate participants' lighting preferences into quantitative lux distributions, which were then used to evaluate alternative designs and make user-centered design decisions. The results of the experimental study show that participants preferred to have maximum simulated daylighting compared to electric lighting. Additionally, participants with some or maximum levels of simulated daylighting performed significantly better on the assigned reading and comprehension tasks than those that did not have any simulated daylighting available. Lastly, by collecting participant personality traits, it was observed that extroverts are significantly more likely to prefer maximum lighting (maximum electric lighting and simulated daylighting) compared to other people. To demonstrate how the collected data and results could be used during the design phase of buildings, as one example, a design case study is presented, in which the design of the same office space (as the experiment) is improved to meet participants' lighting preferences and increase the available simulated daylighting.

1. Introduction The United Nations, the United States, and the European Union have formulated regulations and policies with the goal of significantly reducing the energy consumption and CO2 emissions in all industries, especially in the building industry, which accounts for approximately 40% of the total energy consumption worldwide [18,50,60]. As a result, sustainable solutions (e.g., green building façades, green roofs, etc.), new technologies (e.g., sensors, semi/automated shading and lighting systems, etc.), and operational strategies (e.g., occupancy driven heating/cooling control, etc.) have been integrated into existing and new buildings to reduce the overall energy consumption. Although such improvements have helped to reduce the building energy consumption, in some cases, research has shown that buildings with such improvements do not always operate more efficiently than the conventional buildings in terms of energy consumption. For instance, LEED-certified

⁎

(Leadership in Energy and Environmental Design) buildings were found to be less energy efficient than their conventional counterparts [45]. Differences in occupant behavior (interactions with building energy systems) were identified as the major factor for these inefficiencies in energy consumption, as occupants interact with building energy systems differently in LEED-certified buildings compared to conventional buildings (e.g., overriding automated shading control systems) [25]. Research has identified the lack of understanding about the stochastic changes in occupant behavior as the major factor in the deviations between estimated energy consumption (simulated using building performance simulation (BPS) tools) and actual energy consumption (measured during building operations) [45,66]. In order for buildings to be more energy efficient and the integration of new technologies and sustainable design solutions to be more effective, it is important to understand the relationship between occupant behavior and building energy consumption [43,58]. By using

Corresponding author. E-mail addresses: [email protected] (A. Heydarian), [email protected] (E. Pantazis), [email protected] (A. Wang), [email protected] (D. Gerber), [email protected] (B. Becerik-Gerber). http://dx.doi.org/10.1016/j.autcon.2017.05.003 Received 14 June 2016; Received in revised form 20 March 2017; Accepted 9 May 2017 0926-5805/ © 2017 Elsevier B.V. All rights reserved.

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

building. Despite the emphasis on the importance of UCD, the building industry has not yet widely adopted such technique due to not having enough and accurate information about the actual end-user behaviors and needs. Thus, buildings are usually designed according to the codes and standards that are often based on generalizations with large margins of error rather than according to end-user needs and behaviors. In the recent years, computational capabilities of BPS tools (e.g., Radiance, EnergyPlus, Daysim, TRNSYS, etc.) have provided the design teams with the ability to optimize building designs in order to better meet stakeholder needs and requirements [56]. Due to the increasing complexity in the design of buildings, performance requirements (e.g., LEED certification), and environmental restrictions (and other requirements), BPS platforms are becoming valuable tools to evaluate different design alternatives and solutions [32]. However, to accurately estimate the operational efficiency of a space (or a building), BPS tools would benefit significantly from having access to specific user-related information (e.g., behaviors, needs, comfort levels, etc.). Having access to such information would also assist design teams to make more precise assumptions about occupant behaviors and improve the design of different environments according to the users [2,28,32,70]. For example, by increasing daylighting in a space, we can both improve occupant performance, mood, and well-being while simultaneously reducing the building energy consumption [14,21,33]. However, if buildings are not designed (e.g., orientation of a building, type and size of windows, shading systems, etc.) to meet occupants' comfort levels while harvesting the available daylighting [26,35,61], occupants may feel different levels of discomfort (due to glare, thermal discomfort, etc.) in their environment and prefer to use electric lighting over daylighting, resulting in higher rates of energy consumption [16]. In order to integrate occupant-related information into BPS, design teams usually use general and pre-defined schedules to represent occupant behaviors (e.g., default occupancy and lighting behavior models in EnergyPlus). However, these schedules do not account for the possible changes and differences in occupant behavior from one building to another. Many of these schedules are static and cannot accurately predict the stochastic changes in occupant behavior based on different environmental changes [68]. Prior research has used deterministic and probabilistic approaches for modeling user behaviors in buildings [5,19]. However, to create such models, observational and experimental studies are needed to collect specific information about the changes in occupant behavior based on different influencing factors (external and internal factors) [2,3]. For instance, researchers have collected end-user preferences for available daylighting [67], electric lighting [36,38], window type or sizes [7,14,39], shading configurations [20], and different lighting-related control options [44] in order to gain a better understanding of user interactions with electric lighting and shading systems. However, majority of these studies have focused on examining the influence of external factors on occupant behavior and very few studies have investigated the influence of internal factors on occupant lightingrelated behaviors [27,64]. For instance, psychological factors, such as personality traits, have been reported to directly influence human decision making and changes in behavior [48]. People with similar personality traits have also shown to have similar preferences [9]. Generally, personality is defined as a combination of characteristics and qualities that form a style of thinking, feeling, and behaving in different situations [55]. Rentfrow and Gosling [52] showed that people with “reflective” characteristics and “openness to experiences” have stronger preferences for jazz, blues, and classical music, while more “energetic” people with high degrees of extraversion and agreeableness (personality traits on “The Big Five” personality model) usually prefer hip-hop, rap, or funk music. In another study, [11] showed that people with “openness to experiences” prefer more comedy and fantasy movies, people with “conscientious” characteristics enjoy more action movies, and “neurotic” people tend to like romantic movies. In other words, personality traits can help explain why a certain group of people prefer

BPS tools, design teams and facility managers are able to predict the influence of different physical factors, such as environmental changes (e.g., outside weather, daylighting, indoor and outdoor temperature, etc.), building properties, envelopes, and interior designs on a building's energy consumption. However, lack of understanding about the influence of external (physical factors) and internal (e.g., physiological, psychological, social, etc.) factors on occupant energy consumption behavior have limited the BPS tools to accurately predict the overall building energy consumption [66]. Recent studies have further identified the need for more observational and experimental studies related to occupant behavior in order to understand the correlations between different factors and causation of different behaviors [24]. By identifying such information, behavioral models can be integrated into BPS tools, resulting in more accurate predictions of building energy consumption. To understand the influence of specific factors on occupant behavior, experimental studies provide the opportunity to measure the changes in behaviors by manipulating the variable of interest while controlling for other variables that may influence behavior [29]. However, experimental studies have been identified to be costly and inefficient due to lack of space, time, and money to create physical mock-up environments [24,29]; therefore, researchers primarily rely on the data collected from observational studies to predict different occupant behaviors. Through observational studies, researchers can predict the correlations between different factors and changes in occupant behavior, however without understanding the specific causations of such behaviors (e.g., influence of a specific factor on behavior). Therefore, a systematic approach is missing that can effectively and efficiently be used to collect and measure the changes in occupant behavior based on the identified correlations in observational studies. To further understand the influence of internal factors on occupant behavior, in this paper, we introduce a systematic approach where (1) IVEs are used as an experimental tool to collect participants' lighting preferences; (2) the influence of preferential and psychological factors on participant lighting preferences are analyzed, and (3) different lighting preference profiles and behaviors are predicted. Studies have identified lighting systems account for 40% to 70% of the total electricity consumption in buildings [4,13] (depending on the building purpose and geographical location). Additionally, source of lighting (daylighting vs. artificial lighting) and variations in illuminance levels can significantly influence occupant mood, behavior, performance, and well-being [21,37,62]. Several observational studies have also identified if lighting levels are within occupant preferences, occupants are less likely to interact with lighting and shading systems, which may result in reduction of the lighting-related energy consumption [16,26,61]. Through the proposed approach, we also demonstrate how availability of user lighting preferences can assist design teams evaluate alternative design solutions in order to increase user satisfaction, while reducing the electricity waste associated with electric lighting systems. This demonstration has been provided by redesigning the façade of a single occupancy office space (where user preferences were collected) according to the identified lighting preference profiles. 2. Motivation and gaps in knowledge Research suggests that design teams need to adapt user-centered design (UCD) methods in order to improve the design of buildings around the end-user needs, requirements, and preferences [8]. A few studies have integrated UCD methods into the design of several buildings and found improvements in user satisfaction, as well as building operations [8,69]. For instance, by identifying the needs of future occupants, [46] improved the design of elderly homes to better integrate health status monitoring systems according to the actual enduser privacy preferences and their interactions with different interfaces. This approach also made it easier for the elderly to accept and interact with the integrated technologies and monitoring systems in the 57

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

first number indicates how many lamps are on (e.g., 0 – no lamps on, 3 – all three lamps on); the second, third, and fourth numbers indicate whether each shade is open or closed (from right to left), with 0 indicating that the shade is closed and 1 indicating that the shade is open. In the example shown in Fig. 2, 3100 refers to a lighting system, in which all three lamps are on and only the shade on the right is open. All experiments were conducted between 11:00 AM to 4:00 PM for a period of three months (March–May 2015), where adequate daylight was available with clear sky. This allowed the participants to have the correct perception of the available daylighting in the virtual world in case they chose to open the shades. The 3D models were designed to reflect the sun position at 2:00 PM at the same location (Los Angeles, CA) for the same time of the year (a sunny day on May 2nd). Participants were instructed that after adjusting the lighting levels to their preferred setting, their task would be to read over a passage and answer a few comprehension questions based on what they have read. At the end of the experiment, the participants filled out “The Big Five” personality test [9] and answered a set of questions based on their environmental views and values.

one option over another, and this information can be used to improve personalized services and enhance user experience [34,47]. Once extracted, personality traits can be used to build personality-based user profiles in order to explain different behaviors of occupants and their interactions with different building systems. However, since internal factors (i.e., personality traits) cannot be measured through observational studies, experimental studies are the only approach to collect information about the influence of such factors on occupant behavior. With the recent technological advancements in virtual and augmented reality, researchers in different fields that rely on data visualization, communication, and interactions, such as education, military, psychology, and medical fields have successfully adopted the use of virtual environments to measure human responses and interactions with different spatial and contextual settings [6,12,15,49,51,53]. Similarly, researchers in the building industry have also adopted IVEs for design evaluation [17,30,40–42], education and training purposes [6], and measurements of human behavior in extreme events (e.g., fire and emergency egress) [15]. Previous studies have evaluated whether IVEs are a proper representation of physical environments and if participants have similar sense of presence and immersion in those environments as they do in real-life settings [1,51,53,59]. These studies have found there exist no significant differences between participant performance, presence, and immersion between IVEs and physical environments, and as a result, IVEs can be used as experimental tools to collect user-related information while manipulating different factors of interest. Not only these tools can significantly reduce the cost and inefficiencies associated with experimental studies, but they also allow us to have more control over controlling for or manipulating different variables of interests (i.e., design features, changes in outside weather, interior design, etc.) [10,57]. The presented work in this paper introduces a novel method for collecting end-user behaviors for new buildings and provides a case study as one example to demonstrate how this information could be integrated into the design process. Through the use of IVEs, occupantrelated information can be collected during the design phase and by considering occupant-related parameters, designers can make user centered design decisions.

3.2. IVE setup In order to create a realistic immersive virtual office space, first the room was modeled in Revit© 2015 (Box 1 in Fig. 3). This model only included the basic architecture of the office space including the walls, floor, ceiling, windows, etc. The Revit model was then imported to 3ds Max© in order to add the missing information, such as furniture, material types and properties (e.g., reflection, shadows, etc.), and most importantly the lighting settings. For setting up the lighting maps, the location of the office space was set for Los Angeles, CA and the time was set for 2:00 PM on May 2nd, 2015; the sky and sun conditions were set to be cumulative sky. The 3D model along with the light maps and texture maps were then exported as an FBX file from 3ds Max and imported into Unity 3D game engine (Box 2 in Fig. 3). Fig. 4 shows the workflow for adding the materials, textures, and different maps. Interactive options that allowed the participants to adjust the 3D virtual environment (e.g., ability to turn light switches on or off to control the electric light levels, open or close the shades, etc.) were programmed and assigned to each 3D model. An Oculus DK2 Headmounted Display, an Xbox-360 controller and a position tracking device (tracking the participant's head and neck movements) were connected to the 3D virtual environment and designed to fully track the participant's location and camera angles (Box 4 in Fig. 3). Through this approach, participants could be fully immersed in the virtual environment and interact with the object and control options in the office space. For example, to create a realistic sense of perception, the virtual office space was designed to provide a one-to-one scale sense of feeling as a physical environment would, which allowed the participants to have a clear sense of dimensions in the room. For providing a realistic sense of interaction with the space, we defined boundary boxes where participants had to be located within those boundaries in order to be able to open/close the shades or turn the electric lights on/off. For instance, if a participant wanted to turn on/off the electric lights, he had to virtually walk towards the light switch (located next to the entrance); once he was within the defined boundary box for the light switch, he could then control the electric lighting settings.

3. Methodology The objective of this study is to present a systematic approach, where end-user information (i.e., lighting preferences) is collected for a single occupancy office space and integrated with BPS to understand the influence of participant preferences and personality on their lighting-related behavior. Additionally, the collected data are used to improve the design of the experimental office space. To achieve these objectives, we recruited 90 participants and collected their lighting preferences, environmental views and values, performance, and personality traits. 3.1. Experiment setting A virtual model of an open single occupancy office space, similar in dimensions to an actual office space, was designed and modeled (Fig. 1). The virtual office space was 50 square meters in size. The room included three windows (facing south) along with a manual shading system for each window and 12 light fixtures, where each fixture included three fluorescent lamps. Participants had the freedom to open or close each of the shades individually or turn on/off one, two, or all three light lamps on each light fixture. As a result, participants had the choice of choosing one of the 32 possible combinations between the shading and electric lighting systems as their “preferred lighting setting.” A naming convention is used throughout this paper to allow the readers better understand which lighting settings were chosen by the participants. This naming convention includes 4 numbers, in which the

3.3. Lighting simulation The Unity models (including the imported light maps from 3ds Max) were then passed along to Rhinoceros 3D and Grasshopper for environmental and lighting analyses (Box 3.1 in Fig. 3). By combining “design to analysis” (3D models to BPS), Rhinoceros and Grasshopper provide a single platform where lighting and energy simulations can be performed, analyzed, and visualized. Using the Honeybee and Ladybug plugins in Grasshopper (Box 3.2 in Fig. 3), environmental and lighting 58

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

Fig. 1. Floor plan of the designed virtual office space and different lighting settings (a, b, c).

analyses of the model were performed. Through this approach, the amount of available lighting for each setting was evaluated based on the lighting settings and texture/materials defined in 3ds Max. Ladybug and Honeybee's environmental and lighting tools use three different analysis software (i.e., Energyplus, Daysim, and Open Studio), which provide an access to multiple simulation analyses as well as more control over specific user related parameters (e.g., schedule, behavioral, preferential info) [54] (Box 3.1 in Fig. 3). Three environmental and lighting analyses were performed: (1) daylight factor analysis (DFA), in which simulations were based on annual and daily solar radiation at a given location and orientation; (2) continuous daylight autonomy

Fig. 2. Naming convention used for specific light settings.

Fig. 3. System diagram of the modeling and simulation processes used in this experiment.

59

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

Fig. 4. Render-to-texture workflow for every object in each of the 32 lighting settings.

experiment.

(CDA), which was used to measure daylight autonomy values, and (3) useful daylight luminance (UDI), which helped us understand how we can increase useful daylight luminance (Box 7 in Fig. 3). It is important to note that although our 3D models had the lighting settings for May 2nd 2015 at 2:00 PM in Los Angeles, CA, annual environmental and lighting analyses allowed us to ensure that our lighting settings in 3ds Max were realistic and within standards for the entire year (300–500 lx for office-related activities according to IESNA – Illumination Engineering Society of North America). Lastly, the end-user preferences (Box 5 in Fig. 3), the overall analyses of end-user information (Box 6 in Fig. 3), and the environmental and lighting analysis information (e.g., energy consumption, available daylighting, etc.) (Box 7 in Fig. 3) were used in the design case study to make user-centered design decisions. Through these analyses, we simulated lighting distribution in the room and identified if any changes were needed on the lighting settings defined in 3ds Max. Fig. 5 provides the average lux distributions at the room level as well as the estimated amount of lighting-related electricity in watts (based on florescent 54-watt lamps).

4.1. Participants For this study, 90 participants (56 males and 34 females) were recruited. The participants were undergraduate students (36%) and graduate students (64%) at the University of Southern California (USC) between the ages 18 to 35 (Mean = 22, Median = 24, SD = 3.7). Of these participants, 73% were engineering or architecture majors and 27% were from non-engineering majors (e.g., biology, business, psychology etc.). It is important to note that one of the participants was not able to finish the experiment due to motion sickness and was excused from the study. Therefore, the presented results and analyses are based on the data collected from 89 participants. 4.2. Experiment procedure Prior to running the experiment, participants were provided with a brief description of the experiment, and were asked to sign an IRBapproved consent form. Upon their understanding of the general procedure of the experiment, they were put in a training environment (Fig. 6). This part of the study allowed the participants to become more familiar with the IVE, Xbox controller, navigations, and different virtual interactive options (e.g., turning light switches on, picking objects up). For instance, participants were placed in a “training” virtual office space (different from the one used in the experiment) and instructed to walk through the office, get a feeling of the space, and find objects in the room.

4. Experimental procedure Prior to running the experiment, a pilot study was conducted [31]. In this pilot, we were able to identify the experimental and procedural improvements that were needed or the issues related to the IVE, such as improvements in participant's interactions with the shading systems to control the simulated daylighting, the rendering quality of the virtual space, and the difficulty level and length of the passages used in the

Fig. 5. Average lux distributions at the room level with the estimated lighting-related electricity consumption (assuming electric lights are on from 8:00 AM to 6:00 PM).

60

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

Fig. 6. Illustration of a participant navigating through the training environment to familiarize with immersive virtual environments.

levels of electric lighting. Seventy percent of the participants chose to have all shades open. For these four groups, the average lux distribution in the room, along with the estimated lighting related electricity consumption are provided in Fig. 8. In order to examine if participants were significantly more likely to choose lighting settings with all of the shades open than what was expected by random chance, a chi-square goodness of fit test was conducted. Given only four out of 32 (1/8) of the possible lighting options have all of the shades open, if participants were randomly making their choices, 1/8 of the participants would be expected to choose the lighting options with all of the shades open (“by chance”). The chi-square test confirmed that participants were significantly more likely to choose to have all of the shades open (0111, 1111, 2111, or 3111) than expected by chance (Table 1), χ2 (1, N = 178) = 269.8, p < 0.001. Seventy percent of the participants chose to have all of the shades open, compared to the expected 12% by chance. Among the participants that did not choose to have all three shades open, there was no significant effect of the position of the shades on their preference. The next analysis assessed if participants were more or less likely to have all of the electric lights on than “by chance.” As only 25% of the possible lighting options have all of the electric lights on, if participants were randomly making their choices, 25% of them would be expected to choose the lighting options with all of the electric lights on. The chisquare test confirmed that participants were significantly less likely to choose to have all of the electric lights on than expected by chance (Table 2), χ2 (1, N = 89) = 4.89, p = 0.027. In this analysis, 15% of participants chose to have all of the electric lights on, compared to the expected 25% by chance. An independent sample t-test showed that the participants who had some shades open, read marginally significantly faster than those who had no shades open, (Msome shades open = 2.83 s, SDsome shades open = 0.97, Mno shades open = 2.15 s, SDno shades open = 1.08), t (56) = −1.73, p = 0.089. An independent sample t-test also showed that the participants who had all the shades open and no electric lights on (0111), read significantly faster than those who had some electric lights on, (M0111 = 3.6 s, SD0111 = 1.23, Melectric lights = 2.65 s, SDsome electric lights = 0.93), t (56) = 2.29, p = 0.026. There were no significant differences found in the reading speed and comprehension between the participants who chose to have all electric lights on versus those who chose to have some electric lights on. To study if personality influenced participants' lighting preferences, we analyzed the preferences and five personality traits (the following definitions are borrowed from [9]):

Once participants felt comfortable with the training environment, they were placed in the dark experimental office space. They were instructed that their task was to read a passage and answer several questions based on the passage. They were further instructed that by interacting with the light switch or the shades, they could change the available lighting in the room. Since participants might be interested to see the view outside the windows, they were also told that there is only a blue sky behind the shades with no specific view or scene can be seen. Through this approach we eliminated the possibility that participants would open the shades in order to see the outside view, rather than to increase the daylighting. Participants were placed in a dark virtual office space. In this dark room, they were instructed to choose their most preferred lighting setting. They had a few minutes to make this choice, and were allowed to play with different lighting sources and levels in order to help them make their final choice. Once they felt comfortable with the lighting level, they read the passage and the experimenter recorded the participants reading speed. Afterwards, they were asked to remove the HMD and answer a number of questions based on the passage. Fig. 7 shows a participant setting up the lighting level of the room to her preferred level. Lastly, participants were asked to fill out the personality survey and answer a number of questions related to their environmental views and values. They were then thanked for participating and dismissed from the experiment.

5. Analysis and results Fig. 8 shows the distribution of participants' preferred lighting settings. Participants were put into one of four groups according to the number of electric lamps on. For instance, group 1 contains the participants with preferred lighting settings that did not have any lamps on and group 2 contains the participants with preferred settings that included one light bulb on, and so on. Within each group, the majority of the participants preferred to have all three shades open, indicating that the majority of the participants preferred to have maximum available simulated daylighting along with none or some

• Openness: “reflects a person's tendency to intellectual curiosity, • Fig. 7. A participant adjusting the lighting according to her lighting preferences.

61

creativity and preference for novelty and variety of experiences.” e.g., I see myself as someone who enjoys different types of arts. Conscientiousness: “reflects a person's tendency to show selfdiscipline and aim for personal achievements, and to have an organized and dependable behavior.” e.g., I see myself as someone who does a thorough job.

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

Fig. 8. Lighting-preference distribution, grouped based on the number of turned on lamps.

preferred lighting. This may be due to our sample of participants scoring on average very high on environmentally friendliness (Mean = 4.6 out of 5). Through an OLS linear regression, we also assessed if age had any effects on participants' lighting preferences. As participants increased in age (range 18–35 years old), they were less likely to choose to have all of the shades open, β = −0.14, p = 0.033. No significant effects were found between participants' gender, level of education or field of study and their lighting preferences.

Table 1 All shades vs. not all shades χ2 goodness of fit analysis.

All Shades Not All Shades Total

Observed

Expected

Difference

Diff. Sq./Exp. Fr.

62 27

11 78

51 −51

236.45 33.35 269.80

Table 2 All electric lights on vs. not all electric lights on χ2 goodness of fit analysis.

All Electric Lights Not All Electric Lights Total

Observed

Expected

Difference

Diff. Sq./Exp. Fr.

13 76

22 67

−9 9

3.68 1.21 4.89

6. Design case study In this section, we present one example of how user preferences could be used to improve the design of the same office space that was used in our experiment (Fig. 1). In order to improve the lighting in the office space, we changed the design of the façade to identify the best positions for window openings given the objectives of increasing the amount of daylighting and reducing the lighting-related electricity consumption (while keeping the lux values within the identified lighting preferences from the experimental study). Design alternatives are an essential component to the process of design and engineering of built infrastructure, as competing ideas are put forward and then evaluated for trade-off comparison. In a conventional design process, design and engineering teams would start designing with an initial design scheme and an analysis of it, followed by a sequential re-design and re-analysis process until available resources are exhausted. Instead, we first generated environmental simulations based on the participants' lighting preferences (lux values), which are then used to formulate the boundaries of UDI (useful daylight illuminance) and to evaluate the generated designs. Specifically, we used the top three preference groups (1111, 2111, and 3111), representing 59% of the participants' lighting preferences. First, the lux values were determined based on the Honeybee and Ladybug simulations (Box 3 in Fig. 3). Since the participants in the experiment adjusted the lighting settings of the room to their preferred level to read over the passage located on the table, we used the lux distribution measures on the surface of the table as the participants' “preferred lux level” instead of the entire room's lux distribution (Fig. 9). It is important to also note that since participant lighting preferences were collected in a static lighting environment (2:00 PM on May 2nd 2015 in Los Angeles, CA), in order to provide a conceptual understanding on how having access to end-user lighting preferences can be used in design, for this case study, we made an assumption that the 4 groups of lighting preferences remain the same throughout the year. If dynamic lighting preferences are available, the accuracy of the

• Extraversion: “reflects a person's tendency to seek stimulation in • •

the company of others (showing sociability, talkativeness and assertiveness traits), and to put energy in finding positive emotions, such as happiness, satisfaction, and excitation.” e.g., I see myself as someone is full of energy. Agreeableness: “reflects a person's tendency to be kind, concerned, truthful and cooperative towards others.” e.g., I see myself as someone who is helpful and unselfish with others. Neuroticism: “reflects a person's tendency to experience unpleasant emotions, such as anger, anxiety, depression and vulnerability, and refers to the degree of emotional stability and impulse control.” e.g., I see myself as someone who is ingenious, a deep thinker.

We found that extraverts prefer having high levels of light (either electric light and/or simulated daylight). As people score higher on the extraverted scale, they are significantly more likely to choose to have all of the shades open, β = 0.99, χ2 (1) = 4.96, p = 0.026. They are also marginally significantly more likely to have all of the electric lights on, β = 1.21, χ2 (1) = 3.78, p = 0.052. Additionally, extraverts are marginally more likely to choose the lighting setting 3111 (maximum possible lux setting), β = 1.21, χ2 (1) = 3.78, p = 0.052. We did not find effects of any other personality traits on the participant's lighting choices. Through an OLS (ordinary least square) linear regression, we analyzed whether participants' environmental views and values had any influence on their preferred lighting settings. Environmental views and values did not have a significant effect on the participants' 62

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

Fig. 9. Sample lux distributions in the room and on the table surface.

other factors such as glare, energy consumption, etc.). This design case study is a demonstration of how the end-user information can be integrated into design and engineering practices for increasing certainty and end-user idiosyncrasies in the design phase. Just considering the lighting related electricity consumption, the case study presents an approach how a design team can reduce the use of electric lighting by increasing the available daylighting in space. For instance, in Fig. 10, design 03 (1111), the design alternative shows how the available daylighting in a space can be increased in order to meet an average lux distribution of 600 lx (preferred lighting for group 1111) without the use of any electric lighting. Although, in this case study, factors such as heat gain from daylighting or glare were not considered, the presented case study demonstrates how by having access to endusers' preferential information, design teams can further consider solutions that could potentially increase energy efficiency and user satisfaction. Such preferential and behavioral information could be used to optimize “occupant schedule” along with other settings within BPS, which can further improve energy simulations. Similar userrelated information could be used to define and inform other agencies (e.g., structural agency, thermal comfort agency, etc.) in order to improve design alternatives by factoring in multiple analyses (e.g., environmental, structural, etc.) and user preferences.

simulations would increase. These lux values were used as the initial inputs to a generative multi agent design method. This design method includes a semi-automated generation of façade panels (the architecture and details of the system can be found in [22,23]), where each design requirement is formulated as an agency. The specific design agents that were used included: generative agents (which encode the design intention and geometric properties of the façade panel), specialist agents (which analyze and evaluate the generated designs' performance in terms of lighting), simulation agents (which simulate user preferences and update the generated designs accordingly), and a coordination agent (which develops a communicating and coordinating among agents). In the presented design case study, user preferences are used to inform the “behavior” of the “specialist agents” (they set upper and lower boundaries of UDI as well as the performance that the designed space must meet). The generative agency is defined as a façade panel, which has six design parameters: panel length, panel type percentage, panel extrusion, extrusion type (uniform, non-uniform), placement angle, and tilting factor. These parameters control the behavior of the geometry of the fenestration and therefore, light penetration, light dispersion, and overall daylighting achieved within the office space. Generative agents are responsible for generating façade panel fenestration patterns and use the information provided by the environmental agents, which receive their inputs and boundaries by the lighting analysis results (inputs from Honeybee and Ladybug) and user preferences. At each run, the agents generated alternative designs (different positions and sizes for window openings and different generative paneling angulations and overall fenestration patterns) that perform more optimally in terms of lighting factors, while satisfying user preferences. Fig. 10 shows a sub-set of a series of generated design alternatives for the design of the same office space that was used in the experiment along with the lux distribution in the room. For instance, in the base study (IVE experimental setting), we used conventional windows with a standard size in our design. However, by having access to the participant lighting preference settings (preferred lux distribution), we were able to optimize the available lighting for room settings 1111, 2111, and 3111 to meet the same average lux distribution in the room while increasing the amount of daylighting and decreasing the need for electric lighting. These design alternatives conform to the user preferences while obtaining acceptable CDA and UDI (CDA > 150 lx, 150 lx < UDI < 1500 lx). Through this approach, the designer can narrow down the number of alternatives by further evaluating and identifying the best performing design alternative (possibly considering

7. Discussion The presented framework provides a systematic approach for collecting end-user behavioral information through the use of IVEs. As it is shown in the case study, having access to such information can be use to improve the design of buildings according to the end-user preferences as well as other parameters of interest. Although the presented experiment in this paper has only looked at participants' lighting preferences, more accurate end-user related information (e.g., patterns, shading and lighting control options, window size and shape preferences, etc.) can be collected and used in the design of buildings around the user's needs and preferences. Consistent with the literature on lighting preferences in physical environments [21,63], the findings revealed that the majority of the participants (70%) preferred to have all shades open (maximum simulated daylighting). Thus, in order to meet occupants' lighting preferences with the motivation of reducing the lighting-related electricity consumption, design teams could provide more access to daylighting in built environments in order to motivate end-users to use less electric lighting. Interestingly, only 18% of the participants 63

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

Fig. 10. Front view, isometric and top-view of the existing and improved façade designs.

preferred to have all shades open and all electric lights on (3111 – maximum lighting). This finding shows that when end-users are allowed to set up the lighting levels to their preferred settings, they do not always choose the maximum amount of lighting and are capable of easily performing tasks in lower lux distribution levels. Additionally, some participants chose lighting options that were below the standard requirements. Although standards are in place to ensure occupants can perform certain tasks (without affecting their well-being), these standards may not necessarily reflect what people prefer. The majority of participants not only preferred to have simulated daylighting over electric light, but they also performed better in such environments. Through statistical analysis, we identified that the participants read marginally significantly faster if they chose to have the shades open than when they did not have them open. Similarly, they were able to read significantly faster than those that chose to have only some electric lights on with no shades open. This finding provides extra incentive for building owners to increase the daylight in office spaces in order to possibly increase worker productivity. Additionally, by having access to personality traits, we can better predict occupant preferential and behavioral information and target certain individuals that are more likely to consume energy. For example, we determined that extraverts prefer having maximum

amount of available lighting (both electric and simulated daylighting). Extraverts might use more energy when there is less daylight available, potentially using more electric light. Therefore, by knowing the general purpose of a building, designers might be able to design certain features to better meet future occupants' preferences and needs. For instance, salesman, marketing teams, finance groups, and project managers are expected to be primarily extraverts; therefore, by knowing that a building is being designed to serve people with a certain personality (i.e., extraverts), designers can improve the design of office environments to better meet end-user lighting preferences.

8. Limitations and future work This paper describes a novel data collection approach for userrelated information to be integrated to the design phase of buildings. As part of our future work, we plan to improve the design of our virtual environments by integrating more interactive lighting control options where participants can use to setup their preferred lighting, considering factors such as glare. Although IVE tools are effective to measure human behavior, especially for a newly designed built environment, where the user can interact and get a realistic feeling of a future building, there still exists a number of limitations; for example, the 64

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

The approach introduced in this paper used immersive virtual environments to collect end-user preferences. By having access to this information (as well as user feedback), design teams can translate such information into design boundaries to ensure buildings are designed more around the users. Additionally, by having access to end-user information during the design phase, design teams can perform more accurate energy simulations on the designed environments. As presented in the design case study, with this information, design teams can optimize the lighting settings available in a building such that they not only meet occupants' preferences but also potentially reduce the energy consumption of the building.

environmental settings, available daylighting, and sun location were static elements in our virtual models. However, user's lighting preferences could change at different times of the day or due to different environmental deviations (e.g., change in weather or season). Through our proposed approach, we rendered-to-texture possible lighting combinations and imported the rendered light maps and texture maps into Unity 3D for IVE purposes. Therefore, we did not account for dynamic lighting changes or the angle of view in the virtual environment since the solar illumination of the scenes were preset and static. In our future studies, we will address these limitations by performing real-time lighting rendering at different hours of the days and times of the year to better understand the changes in participants' lighting preferences. We plan to follow the methodology in this paper for integrating dynamic simulations by changing the sun location, sky conditions, etc. We identified age might have an influence on participants' lighting preferences. However, in this study the age of the participants ranged from 18 to 35 years old. The effects may differ with a larger age range, an interesting question worth further investigation. Similarly, it is also important to increase the sample diversity, including users with different education levels and environmental views and values. In addition, if the sample size/diversity are increased, we might be able to quantify the distribution of preferences across a population, for which spaces could be designed adequately (eliminating the issue of new occupants of a building). Increase in sample size would also allow us to gather participants' lighting preferences at different hours of a day and time of the year. Another solution to this issue is to design building facades that are adaptive and responsive (e.g., kinetic facades), where the façade elements could dynamically be adjusted based on end-user's lighting preferences. Although in the presented case study, the goal was to increase the available daylighting to meet participants' lighting preferences (groups 1111, 2111, and 3111) while reducing the need for electric lighting, we did not consider other factors, such as heat gain from increased daylighting and glare that could influence end-users' choice of lighting. Such factors could significantly influence the energy consumption in a building. As part of our future work, we plan to evaluate the tradeoff between increase in daylighting and its influence on heat gain to provide more accurate user-related information. Lastly, previous studies suggest that social factors (e.g., presence of other people) influence occupant's lighting choices in an office [65]. In this paper, we focused on a single occupancy office space where the participants did not have to consider other people's lighting preferences while setting up the lighting levels to their most preferred settings. In our future studies, we plan to investigate preferences in social contexts.

Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. 1231001. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Special thanks to all the participants and researchers that contributed to this project; specifically, Joao Carneiro for assisting to create 3D models and Saba Khashe for her contribution on preparing and running the experiments. References [1] M. Adi, D. Roberts, Using virtual environments to test the effects of lifelike architecture on people, Technologies of Inclusive Well-being, vol. 536, Springer, Berlin Heidelberg, 2014, pp. 261–285. [2] D. Aerts, J. Minnen, I. Glorieux, I. Wouters, F. Descamps, A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison, Build. Environ. 75 (2014) 67–78. [3] R.V. Andersen, J. Toftum, K.K. Andersen, B.W. Olesen, Survey of occupant behaviour and control of indoor environment in Danish dwellings, Energ. Buildings 41 (1) (2009) 11–16. [4] M. Ashe, M. de Monasterio, M. Gupta, M. Pegors, 2010 US lighting market characterization, Report to US Department of Energy, 2012. [5] S. Borgeson, G. Brager, Occupant Control of Windows: Accounting for Human Behavior in Building Simulation, UC Berkeley: Center for the Built Environment, 2008. [6] P.M. Bosch-Sijtsema, J. Haapamäki, Perceived enablers of 3D virtual environments for virtual team learning and innovation, Comput. Hum. Behav. 37 (2014) 395–401. [7] M. Boubekri, R.B. Hull, L.L. Boyer, Impact of window size and sunlight penetration on office workers' mood and satisfaction a novel way of assessing sunlight, Environ. Behav. 23 (4) (1991) 474–493. [8] H.-J. Bullinger, W. Bauer, G. Wenzel, R. Blach, Towards user centred design (UCD) in architecture based on immersive virtual environments, Comput. Ind. 61 (4) (2010) 372–379. [9] I. Cantador, I. Fernández-Tobías, A. Bellogín, Relating personality types with user preferences in multiple entertainment domains, in: Shlomo Berkovsky (Ed.), CEUR Workshop Proceedings, 2013. [10] C.-S. Chan, C.-H. Weng, How real is the sense of presence in a virtual environment? Applying protocol analysis for data collection, Digital Opportunities: Proceedings of the 10th International Conference on Computer-Aided Architectural Design Research in Asia, vol. 1, Architexturez Imprints, New Delhi, 2005, pp. 188–197. [11] O. Chausson, Who watches what? Assessing the impact of gender and personality on film preferences, Paper published online on the MyPersonality project website http://mypersonality.org/wiki/doku.php, (2010). [12] C. De Lillo, F.C. James, Spatial working memory for clustered and linear configurations of sites in a virtual reality foraging task, Cogn. Process. 13 (1) (2012) 243–246. [13] T.G. Dietterich, Ensemble methods in machine learning, Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings, Springer Berlin Heidelberg, Berlin, Heidelberg, 2000, pp. 1–15. [14] I.T. Dogrusoy, M. Tureyen, A field study on determination of preferences for windows in office environments, Build. Environ. 42 (10) (2007) 3660–3668. [15] E. Duarte, F. Rebelo, J. Teles, M.S. Wogalter, Behavioral compliance for dynamic versus static signs in an immersive virtual environment, Appl. Ergon. 45 (5) (2014) 1367–1375. [16] M.-C. Dubois, Å. Blomsterberg, Energy saving potential and strategies for electric lighting in future North European, low energy office buildings: a literature review, Energ. Buildings 43 (10) (2011) 2572–2582. [17] P.S. Dunston, L.L. Arns, J.D. Mcglothlin, G.C. Lasker, A.G. Kushner, An immersive virtual reality mock-up for design review of hospital patient rooms, Collaborative Design in Virtual Environments, Springer, 2011, pp. 167–176. [18] U.S. EIA, Energy information administration: how much energy is consumed in residential and commercial buildings in the united states? Last accessed August 2015, Available at http://www.eia.gov/tools/faqs/faq.cfm?id=86&t=1, (2015). [19] V. Fabi, R.V. Andersen, S.P. Corgnati, B.W. Olesen, M. Filippi, Description of

9. Conclusions In this study, we introduced a novel approach for collecting enduser preferences for a building under design. Eighty-nine participants' lighting preferences, performance (reading speed and comprehension), personality traits, and environmental views and values were collected and analyzed. The results showed that the participants significantly more prefer to have simulated daylight (have all shades open) as part of their preferred lighting setting and perform better in such condition. Additionally, extraverts were found to be significantly more likely to prefer maximum lighting (maximum electric and simulated daylighting). The findings could help design teams create spaces around the end-users' needs and preferences, which could result in higher user satisfaction, increased work productivity, and possibly a reduction in energy consumption. To demonstrate how the collected data and analyses could be used during the design phase of buildings, we presented a case study where we re-designed the office space used in our experiments, according to the participant's lighting preferences and increased the available simulated daylighting. The presented case study provides an example towards integrating end-user preference information during the design phase. 65

Automation in Construction 81 (2017) 56–66

A. Heydarian et al.

[20] [21]

[22]

[23]

[24]

[25] [26]

[27]

[28] [29]

[30]

[31]

[32] [33]

[34] [35] [36] [37]

[38]

[39] [40]

[41]

[42]

[43]

[44]

office lighting, Light. Res. Technol. 35 (1) (2003) 43–57. [45] G. Newsham, B. Birt, C. Arsenault, L. Thompson, J. Veitch, S. Mancini, A. Galasiu, B. Gover, I. Macdonald, G. Burns, Do green buildings outperform conventional buildings? Indoor Environment and Energy Performance in North American Offices, 2012. [46] L.E. Nugroho, L. Lazuardi, R. Ferdiana, Perspectives of human centered design and interoperability in ubiquitous home care for elderly people, Electrical Engineering and Informatics (MICEEI), 2014 Makassar International Conference on, IEEE, 2014, pp. 118–123. [47] M.A.S.N. Nunes, Recommender Systems Based on Personality Traits, Université Montpellier II-Sciences et Techniques du Languedoc, 2008. [48] M.A.S.N. Nunes, R. Hu, Personality-based recommender systems: an overview, Proceedings of the Sixth ACM Conference on Recommender Systems, ACM, Dublin, Ireland, 2012, pp. 5–6. [49] T. Parsons, Virtual teacher and classroom for assessment of neurodevelopmental disorders, in: A.L. Brooks, S. Brahnam, L.C. Jain (Eds.), Technologies of Inclusive Well-being, vol. 536, Springer, Berlin Heidelberg, 2014, pp. 121–137. [50] L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energ. Buildings 40 (3) (2008) 394–398. [51] D.-P. Pertaub, M. Slater, C. Barker, An experiment on public speaking anxiety in response to three different types of virtual audience, Presence Teleop. Virt. 11 (1) (2002) 68–78. [52] P.J. Rentfrow, S.D. Gosling, The do re mi's of everyday life: the structure and personality correlates of music preferences, J. Pers. Soc. Psychol. 84 (6) (2003) 1236. [53] D. Roberts, I. Heldal, O. Otto, R. Wolff, Factors influencing flow of object focussed collaboration in collaborative virtual environments, Virtual Reality 10 (2) (2006) 119–133. [54] M. Roudsari, M. Pak, A. Smith, Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design, Proceedings of 13th Conference of International Building Performance Simulation Association Chambery, France August 26–28, 2014. [55] D.L. Schacter, D.T. Gilbert, D.M. Wegner, Psychology, second ed., Worth, New York, 2011. [56] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renew. Sust. Energ. Rev. 34 (2014) 409–429. [57] M.F. Shiratuddin, W. Thabet, D. Bowman, Evaluating the effectiveness of virtual environment displays for reviewing construction 3D models, CONVR 2004, 2004, pp. 87–98. [58] A. Sieminski, International energy outlook, US Energy Information Administration (EIA) Report Number: DOE/ELA-0484, 2013. [59] M. Slater, Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments, Philos. Trans. R. Soc. Lond. B 364 (1535) (2009) 3549–3557. [60] L. Soriano Marcolino, H. Xu, D.J. Gerber, B. Kolev, S. Price, E. Pantazis, M. Tambe, Agent Teams for Design Problems, AAMAS, 2015. [61] K. Van Den Wymelenberg, Patterns of occupant interaction with window blinds: a literature review, Energ. Buildings 51 (2012) 165–176. [62] J. Veitch, G. Newsham, P. Boyce, C. Jones, Lighting appraisal, well-being and performance in open-plan offices: a linked mechanisms approach, Light. Res. Technol. 40 (2) (2008) 133–151. [63] J.A. Veitch, D.W. Hine, R. Gifford, End users' knowledge, beliefs, and preferences for lighting, J. Inter. Des. 19 (2) (1993) 15–26. [64] J. VON GRABE, A Psychological Approach to Understanding and Predicting Energyrelevant Human Interaction with Buildings, (2016). [65] I. Vorapat, Balancing comfort: occupants' control of window blinds in private offices, Architecture, University of California, Berkeley, 2005. [66] D. Yan, W. O'Brien, T. Hong, X. Feng, H. Burak Gunay, F. Tahmasebi, A. Mahdavi, Occupant behavior modeling for building performance simulation: current state and future challenges, Energ. Buildings 107 (2015) 264–278. [67] X. Yu, Y. Su, Daylight availability assessment and its potential energy saving estimation–a literature review, Renew. Sust. Energ. Rev. 52 (2015) 494–503. [68] Z. Yu, B.C.M. Fung, F. Haghighat, H. Yoshino, E. Morofsky, A systematic procedure to study the influence of occupant behavior on building energy consumption, Energ. Buildings 43 (6) (2011) 1409–1417. [69] M. Zahedi, M. Guité, G. De Paoli, Addressing user-centeredness: communicating meaningfully through design, Designing Together, CAADFutures, 2011. [70] J. Zhao, B. Lasternas, K.P. Lam, R. Yun, V. Loftness, Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining, Energ. Buildings 82 (2014) 341–355.

occupant behaviour in building energy simulation: state-of-art and concepts for improvements, Building Simulation 2011 - 12th Conference of Interantional Building Performance Simulation Association, Sydney, 14-16 November, 2011. M. Foster, T. Oreszczyn, Occupant control of passive systems: the use of venetian blinds, Build. Environ. 36 (2) (2001) 149–155. A.D. Galasiu, J.A. Veitch, Occupant preferences and satisfaction with the luminous environment and control systems in daylit offices: a literature review, Energ. Buildings 38 (7) (2006) 728–742. D. Gerber, E. Pantazis, Design exploring complexity in architectural shells interactive form finding of reciprocal frames through a multi-agent system, Proceedings of the 34th eCAADe Conference, 2016, pp. 455–464. D.J. Gerber, E. Pantazis, A. Wang, A multi-agent approach for performance based architecture: design exploring geometry, user, and environmental agencies in façades, Autom. Constr. 76 (2017) 45–58. S. Gilani, W. O'Brien, Review of current methods, opportunities, and challenges for in-situ monitoring to support occupant modelling in office spaces, J. Build. Perform. Simul. (2016) 1–27. Z.M. Gill, M.J. Tierney, I.M. Pegg, N. Allan, Low-energy dwellings: the contribution of behaviours to actual performance, Build. Res. Inf. 38 (5) (2010) 491–508. H.B. Gunay, W. O'Brien, I. Beausoleil-Morrison, A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices, Build. Environ. 70 (2013) 31–47. H.B. Gunay, W. O'Brien, I. Beausoleil-Morrison, Implementation and comparison of existing occupant behaviour models in EnergyPlus, J. Build. Perform. Simul. 9 (6) (2016) 567–588. F. Haldi, D. Robinson, The impact of occupants' behaviour on building energy demand, J. Build. Perform. Simul. 4 (4) (2011) 323–338. A. Heydarian, B. Becerik-Gerber, Use of immersive virtual environments for occupant behaviour monitoring and data collection, J. Build. Perform. Simul. (2016) 1–15. A. Heydarian, J.P. Carneiro, D. Gerber, B. Becerik-Gerber, Immersive virtual environments, understanding the impact of design features and occupant choice upon lighting for building performance, Build. Environ. 89 (2) (2015) 217–228. A. Heydarian, E. Pantazis, J.P. Carneiro, D. Gerber, B. Becerik-Gerber, Towards understanding end-user lighting preferences in office spaces by using immersive virtual environments, J. Comput. Civ. Eng. 3 (2015) 475–482. P. Hoes, J.L.M. Hensen, M.G.L.C. Loomans, B. de Vries, D. Bourgeois, User behavior in whole building simulation, Energ. Buildings 41 (3) (2009) 295–302. T. Hong, S. D'Oca, W.J.N. Turner, S.C. Taylor-Lange, An ontology to represent energy-related occupant behavior in buildings. Part I: introduction to the DNAs framework, Build. Environ. 92 (2015) 764–777. R. Hu, P. Pu, A study on user perception of personality-based recommender systems, User Modeling, Adaptation, and Personalization, Springer, 2010, pp. 291–302. V. Inkarojrit, Balancing Comfort: Occupants' Control of Window Blinds in Private Offices, (2005). H. Juslén, M. Wouters, A. Tenner, Preferred task-lighting levels in an industrial work area without daylight, Light. Res. Technol. 37 (3) (2005) 219–231. R. Küller, S. Ballal, T. Laike, B. Mikellides, G. Tonello, The impact of light and colour on psychological mood: a cross-cultural study of indoor work environments, Ergonomics 49 (14) (2006) 1496–1507. C. Laurentin, V. Bermtto, M. Fontoynont, Effect of thermal conditions and light source type on visual comfort appraisal, Light. Res. Technol. 32 (4) (2000) 223–233. P. Leather, M. Pyrgas, D. Beale, C. Lawrence, Windows in the workplace sunlight, view, and occupational stress, Environ. Behav. 30 (6) (1998) 739–762. R.M. Leicht, P.M. Abdelkarim, J.I. Messner, Gaining end user involvement through virtual reality mock-ups: a medical facility case study, Proceedings of the CIB W, 78 2010, p. 2010. T. Majumdar, M.A. Fischer, B.R. Schwegler, Conceptual design review with a virtual reality mock-up model, Joint International Conference on Computing and Decision Making in Civil and Building Engineering, 2006. K.D. Maldovan, J.I. Messner, M. Faddoul, Framework for reviewing mockups in an immersive environment, CONVR 2006: 6th International Conference on Construction Applications of Virtual Reality, Orlando, Florida, August, 2006, pp. 3–4. L.S. Marcolino, B. Kolev, S. Price, S.P. Veetil, D. Gerber, J. Musil, M. Tambe, Aggregating opinions to design energy-efficient buildings, 8th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF 2014), Quebec, Canada, 2014. T. Moore, D. Carter, A. Slater, Long-term patterns of use of occupant controlled

66