Occupant Behavior and Building Energy Consumption



Research Overview

This research focuses on how the design and operation of buildings can be improved by centering them around occupants’ preferences and behaviors while simultaneously reducing the overall energy consumption. In recent years, studies have identified that occupant behavior and preferences significantly influence the total energy consumption in buildings. However, due to the lack of available occupant-related information and more importantly, lack of an understanding of the specific relationships between design features and energy consumption, user needs and requirements have not been integrated into the design and operation of buildings; ultimately, this has resulted in inefficient system operations and higher energy consumption during the operation phase of buildings. To address this issue, in this research, a systematic approach is defined to (I) collect a large amount of occupant-related data (behavioral and preferential) in different designs and operational settings, (II) identify the relationships between the collected occupant-data and energy consumption, and (III) reason how such relationships can be used to improve the design and operation of buildings. In order to gather occupant-related data, an innovative approach is introduced to collect users’ behavioral and preferential information by immersing them in realistically designed virtual environments and allowing them to interact with different design features (i.e., artificial lighting systems, blinds, control options). To ensure the collected data in Immersive Virtual Environments (IVEs) is a proper representation of physical settings, a number of benchmarking studies were conducted to identify if there is a significant difference between participants’ productivity, sense of presence, perception, and immersion between the two mediums (IVE vs. physical environment). After identifying that IVEs can be used to collect realistic occupant-related information, three IVE-based experimental studies were conducted, in which (1) the influence of semi-automated lighting and shading control options on participants’ choice of lighting (daylighting vs. electric lighting) in a work environment were analyzed; (2) the participant lighting preferences were collected while performing office-related activities and identified how personality traits and environmentally friendliness may influence choice of lighting; and (3) psychological concepts, such as default settings, were implemented in order to influence occupants’ propensity to keep or adjust lighting and shading settings with the objective of reducing the energy consumption in buildings. By collecting a large number of participant data, a set of behavioral and preferential profiles were defined based on the relationship between lighting preferences, performance, personality traits, and participant interactions with the lighting and shading systems. These user-profiles were then integrated into a Building Performance Simulations (BPS) tool to identify their influences on the total energy consumption in buildings. The defined systematic approach allows for effectively integrating user-centered thinking into design and operation of buildings to better meet the occupants’ needs and preferences while reducing the building energy consumption.


In this research, an innovative approach was introduced to improve the design and operation of buildings to be centered around the occupants’ needs and behaviors whileprefernce-default-vr reducing the overall energy consumption. To achieve this, users’ behavioral and preferential information are collected and integrated into building performance simulations (BPS) for design and operational improvements and decision making. Through the use of immersive virtual environments (IVEs), three experimental studies were performed where users’ behavioral and preferential data were collected in order to identify the relationship between the users and buildings’ energy consumption. In the first study, the influence of semi-automated lighting control options on participants’ choice of lighting (natural vs. electric lighting) was evaluated with the objective of motivating occupants to use more daylighting in their work environment. In the second study, participants’ lighting preferences were collected through the use of IVEs. Further their personality traits, environmental friendliness, and performance in their preferred lighting settings were collected and analyzed to identify if any relationships between their behaviors and lighting preferences exists. Additionally, through the use of BPS, each participant’s lighting preferences were evaluated by calculating and visualizing the corresponding lighting distribution and changes in energy consumption. In the third study,  participants’ propensity to keep or adjust the default lighting settings were analyzed in order to better understand the design and operational factors that influence the user behaviors. The collected behavioral and preferential data from these experiments are then imported to BPS to further understand the impact of user’s preferences and behaviors on the total energy consumption in a given space. Through these studies, not only the relationship between users’ lighting preferences and behaviors with the amount of energy consumption was demonstrated, but also a systematic approach to collect realistic end-user information through the use of IVEs was introduced.


Lighting Control Options – Study 1

In this study, 114 participants were recruited where the influence of manual and semi-automated (remote) lighting control options on participants’ interactions with the shading and electric lighting systems were measured in an immersive virtual environment. A single-occupancy IVE office space was created and participants were randomly assigned to one of the following groups: group 1: the participants had manual control options to control the electric lighting and shading systems (similar to most offices); group 2: the participants in this group had the option of remotely controlling only the shading system; group 3: the remote option was used to control only the electric lighting system; and group 4: the participants were given the option to remotely control both the electric and shading systems. The results showed that participants were significantly more likely to use the remote control options over the manual options when the remote control option(s) was presented to them (groups 2, 3, and 4). Additionally, when the remote control option was provided to them to adjust only the shading system (group 2), they were significantly more likely to use simulated daylighting over the electric lighting. However, when they were provided with the remote control options for both the shading and electric lighting systems (group 4), they were significantly less likely to use the simulated daylighting over electric lighting. We concluded that semi-automated remote control options could be used to motivate occupants to use daylighting over electric lighting in single-occupancy office spaces.



Lighting Preferences  – Study 2

Through introducing an IVE-based data collection approach, participants’ lighting preferences were collected and translated into quantitative measures using environmental and lighting simulation programs. Ninety participants were placed in a dark virtual office space and were given the option to setup the room’s lighting based on their preferences by interacting with the shading and electric lighting systems. The results of the study showed that approximately 70% of the participants preferred to have maximum simulated daylighting along with no or some levels of electric lighting. Additionally, as part of this study, the relationship between participants’ lighting preferences and personality traits were analyzed, through which it was discovered that extroverts are significantly more likely to prefer maximum lighting (maximum electric lighting and simulated daylighting) in their work environment. The data collection approach along with the findings of this study demonstrated how occupant’s preferences can be collected through the use of IVEs and can be translated into meaningful quantitative information through the use of building performance simulations, which could be used by the design teams and building managers to improve the design and operation of buildings.



Default Lighting Settings  – Study 3

In this study, participants’ propensity to keep or adjust the default lighting settings were measured based on the level and availability of simulated daylighting and electric lighting in the virtual office space (similar to study 2). 160 participants were randomly assigned in one of the five default groups (shown below); each group had either maximum electric lighting, maximum simulated daylighting or different combinations of electric lighting and simulated daylighting settings. The study’s results showed that participants were significantly more likely to keep the default lighting setting if simulated daylighting was available as part of the default settings compared to when only electric lighting was available as a default. The results also showed that participants performed better in the conditions where simulated daylighting was available. By analyzing the relationship between participants’ personality and their propensity to keep or adjust the default setting, it was discovered that people with neuroticism had a higher tendency to keep the default setting when simulated daylighting was available compared to the settings that only electric lighting was available. The majority of the participants preferred daylighting in their office environments and if daylighting was available prior to their arrival (i.e., shades are open), they would have a higher tendency to keep the shades open and use less artificial lighting. Through such psychological techniques, facility managers can define user-centered approaches to increase satisfaction of building occupants (e.g., meeting lighting preferences by increasing the available daylighting) while reducing the energy consumption in buildings (e.g., reducing the need for using electric lighting).



Related Publication:

  1. Heydarian, A., Pantazis, E., Wang, A., Gerber, D., and Becerik-Gerber, B. (2017) “Towards User Centered Building Design: Identifying End-user Lighting Preferences via Immersive Virtual Environments.” Automation in Construction.
  2. Heydarian, A., Becerik-Gerber, B. (2016).  “Use of Immersive Virtual Environments for Occupant Behaviour Monitoring and Data Collection.” Journal of Building Performance Simulation.
  3. Heydarian, A., Pantazis, E., Carneiro, J.P., Gerber, D., and Becerik-Gerber, B. (2016) “Lighting, Building, Action: Impact of Default Settings on Occupant Behaviour.” Elsevier Journal of Environmental Psychology.
  4. Khashe, S., Heydarian, A., Becerik-Gerber, B., and Wood, W. (2016) “Exploring the Effectiveness of Social Messages on Promoting Energy Conservation Behavior in Buildings.” Building and Environment.
  5. Khashe, S., Heydarian, A., Gerber, D., Becerik-Gerber, B., Hayes, T., and Wood, W. (2015). “Influence of LEED branding on building Occupants’ pro-environmental behavior.” Elsevier Journal of Building and Environment.
  6. Heydarian, A., Pantazis, E., Gerber, D., and Becerik-Gerber, B. (2016). “Defining Lighting Settings to Accommodate End-user Preferences While Reducing Energy Consumption in Buildings.” Proc. Construction Research Congress, Puerto Rico.
  7. Heydarian, A., Carneiro, J.P., Pantazis, E., Gerber, D., and Becerik-Gerber, B. (2015). “Default Conditions: A Reason for Design to Integrate Human Factors.” Sustainable Human-Building Ecosystems, pp, 54-62.
  8. Heydarian, A., Pantazis, E., Carneiro, J.P., Gerber, D., and Becerik-Gerber, B. (2015). “Towards Understanding End-user Lighting Preferences in Office Spaces by Using Immersive Virtual Environments”. The International Workshop on Computing in Civil Engineering, Austin, TX.
  9. Gerber, D., Pantazis, E., Marcolino, L., Heydarian, A., (2015). “A Multi Agent Systems for Design Simulation Framework: Experiments with Virtual Physical Social Feedback for Architecture” The Symposium on Simulation for Architecture and Urban Design (SimAUD), Pages 205-212.