Skip to main content
SpringerLink
Log in

Simulation and visualization of energy-related occupant behavior in office buildings

  • Research Article
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • ASHRAE (2013a). ANSI/ASHRAE Standard 62.1-2013: Ventilation for Acceptable Indoor Air Quality. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • ASHRAE (2013b). ANSI/ASHRAE Standard 90.1-2013: Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.

    Google Scholar 

  • Belafi Z, Hong T, Reith A (2016). A library of building occupant behavior models represented in a standardized schema. In: Proceedings of BEHAVE 2016, the 4th European Conference on Behaviour and Energy Efficiency, Coimbra, Portugal.

    Google Scholar 

  • Bonte M, Thellier F, Lartigue B (2014). Impact of occupant’s actions on energy building performance and thermal sensation. Energy and Buildings, 76: 219–227.

    Article  Google Scholar 

  • Chen Y, Gu L, Zhang J (2015). EnergyPlus and CHAMPS-Multizone co-simulation for energy and indoor air quality analysis. Building Simulation, 8: 371–380.

    Article  Google Scholar 

  • Chen Y, Raphael B, Sekhar SC (2012). Individual control of a personalized ventilation system integrated with an ambient mixing ventilation system. HVAC&R Research, 18: 1136–1152.

    Google Scholar 

  • Chen Y, Raphael B, Sekhar SC (2016a). Experimental and simulated energy performance of a personalized ventilation system with individual airflow control in a hot and humid climate. Building and Environment, 96: 283–292.

    Article  Google Scholar 

  • Chen Y, Luo X, Hong T (2016b). An agent-based occupancy simulator for building performance simulation. In: Proceedings of ASHRAE Annual Conference, St. Louis, USA.

    Google Scholar 

  • Chiang W-H, Wang C-Y, Huang J-S (2012). Evaluation of cooling ceiling and mechanical ventilation systems on thermal comfort using CFD study in an of fi ce for subtropical region. Building and Environment, 48: 113–127.

    Article  Google Scholar 

  • Crawley DB, Lawrie LK, Winkelmann FC, Buhl WF, Huang YJ, et al. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.

    Article  Google Scholar 

  • Feng X, Yan D, Hong T (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348–359.

    Article  Google Scholar 

  • Gunay HB, O’Brien W, Beausoleil-Morrison I (2016). Implementation and comparison of existing occupant behaviour models in EnergyPlus. Journal of Building Performance Simulation, 9: 567–588.

    Article  Google Scholar 

  • Hoes P, Hensen JLM, Loomans MGLC, de Vries B, Bourgeois D (2009). User behavior in whole building simulation. Energy and Buildings, 41: 295–302.

    Article  Google Scholar 

  • Hong T, D’Oca S, Taylor-Lange SC, Turner WJN, Chen Y, Corgnati SP (2015a). An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema. Building and Environment, 94: 196–205.

    Article  Google Scholar 

  • Hong T, D’Oca S, Turner WJN, Taylor-Lange SC (2015b). An ontology to represent energy-related occupant behavior in buildings Part I: Introduction to the DNAs framework. Building and Environment, 92: 764–777.

    Article  Google Scholar 

  • Hong T, Sun H, Chen Y, Taylor-Lange SC, Yan D (2016a). An occupant behavior modeling tool for co-simulation. Energy and Buildings, 117: 272–281.

    Article  Google Scholar 

  • Hong T, Taylor-Lange SC, D’Oca S, Yan D, Corgnati SP (2016b). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116: 694–702.

    Article  Google Scholar 

  • Lee YS, Malkawi AM (2014). Simulating multiple occupant behaviors in buildings: An agent-based modeling approach. Energy and Buildings, 69: 407–416.

    Article  Google Scholar 

  • Luo X (2016). Performance Evaluation and Enhancement of a Probabilistic Occupancy Simulation Model. Carnegie Mellon University, USA.

    Google Scholar 

  • Luo X, Lam KP, Chen Y, Hong T (2017). Performance Evaluation of an Agent-based Occupancy Simulation Model. Building and Environment, 2017, 115: 42–53.

    Google Scholar 

  • Mahdavi A, Tahmasebi F (2015). Predicting people’s presence in buildings: An empirically based model performance analysis. Energy and Buildings, 86: 349–355.

    Article  Google Scholar 

  • O’Brien W, Kapsis K, Athienitis AK (2013). Manually-operated window shade patterns in office buildings: A critical review. Building and Environment, 60: 319–338.

    Article  Google Scholar 

  • Okazaki S, Matsushita S (1993). A study of simulation model for pedestrian movement with evacuation and queuing. In: Proceedings of International Conference on Engineering for Crowd Safety.

    Google Scholar 

  • Page J, Robinson D, Morel N, Scartezzini JL (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40: 83–98.

    Article  Google Scholar 

  • Pilkington B, Roach R, Perkins J (2011). Relative benefits of technology and occupant behaviour in moving towards a more energy efficient, sustainable housing paradigm. Energy Policy, 39: 4962–4970.

    Article  Google Scholar 

  • Reinhart CF (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77: 15–28.

    Article  Google Scholar 

  • Richardson I, Thomson M, Infield D (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40: 1560–1566.

    Article  Google Scholar 

  • Socolow RH (1978). The twin rivers program on energy conservation in housing: Highlights and conclusions. Energy and Buildings, 1: 207–213.

    Article  Google Scholar 

  • Stoppel CM, Leite F (2014). Integrating probabilistic methods for describing occupant presence with building energy simulation models. Energy and Buildings, 68: 99–107.

    Article  Google Scholar 

  • Sun K, Yan D, Hong T, Guo S (2014). Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration. Building and Environment, 79: 1–12.

    Article  Google Scholar 

  • Tahmasebi F, Mahdavi A (2015). The sensitivity of building performance simulation results to the choice of occupants’ presence models: A case study. Journal of Building Performance Simulation, doi: 10.1080/19401493.2015.1117528.

    Google Scholar 

  • US DOE (2016). EnergyPlus Testing and Validation. Available at https://energyplus.net/testing. Accessed 9 Jan 2016.

    Google Scholar 

  • Virote J, Neves-Silva R (2012). Stochastic models for building energy prediction based on occupant behavior assessment. Energy and Buildings, 53: 183–193.

    Article  Google Scholar 

  • Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.

    Article  Google Scholar 

  • Wang LL, Dols WS, Chen Q (2010). Using CFD Capabilities of CONTAM 3.0 for simulating airflow and contaminant transport in and around buildings. HVAC&R Research, 16: 749–763.

    Article  Google Scholar 

  • Wetter M (2011). Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed. Journal of Building Performance Simulation, 4: 185–203.

    Article  Google Scholar 

  • Yan D, O’Brien W, Hong T, Feng X, Gunay HB, Tahmasebi F, Mahdavi A (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264–278.

    Article  Google Scholar 

  • Yan D, Xia J, Tang W, Song F, Zhang X, Jiang Y (2008). DeST—An integrated building simulation toolkit Part I: Fundamentals. Building Simulation, 1: 95–110.

    Article  Google Scholar 

Download references

Acknowledgements

This study is supported by the Assistant Secretary for Energy Efficiency and Renewable Energy of the United States Department of Energy under Contract No. DE-AC02-05CH11231 through the U.S.-China joint program of Clean Energy Research Center on Building Energy Efficiency. This work is also part of the research activities of IEA EBC Annex 66, definition and simulation of occupant behavior in buildings.

Author information

Authors and Affiliations

  1. Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Yixing Chen, Tianzhen Hong & Xuan Luo

  2. School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China

    Xin Liang

Authors
  1. Yixing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianzhen Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tianzhen Hong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Liang, X., Hong, T. et al. Simulation and visualization of energy-related occupant behavior in office buildings. Build. Simul. 10, 785–798 (2017). https://doi.org/10.1007/s12273-017-0355-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-017-0355-2

Keywords

Search

Navigation