Building Simulation

, Volume 10, Issue 6, pp 799–810 | Cite as

Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls

  • Panyu Zhu
  • Michael Gilbride
  • Da YanEmail author
  • Hongshan Sun
  • Christopher MeekEmail author
Research Article


As building owners, designers, and operators aim to achieve significant reductions in overall energy consumption, understanding and evaluating the probable impacts of occupant behavior becomes a critical component of a holistic energy conservation strategy. This becomes significantly more pronounced in ultra-efficient buildings, where system loads such as heating, cooling, lighting, and ventilation are reduced or eliminated through high-performance building design and where occupant behavior-driven impacts reflect a large portion of end-use energy. Further, variation in behavior patterns can substantially impact the persistence of any performance gains. This paper describes a methodology of building occupant behavior modeling using simulation methods developed by the Building Energy Research Center (BERC) at Tsinghua University using measured energy consumption data collected by the University of Washington Integrated Design Lab (UW IDL). The Bullitt Center, a six-story 4831 m2 (52,000 ft2) net-positive-energy urban office building in Seattle, WA, USA, is one of the most energy-efficient buildings in the world (2013 WAN Sustainable Building of the Year Winner). Its measured energy consumption in 2015 was approximately 34.8 kWh/(m2∙yr) (11 kBtu/(ft2∙yr)). Occupant behavior exerts an out-sized influence on the energy performance of the building. Nearly 33% of the end-use energy consumption at the Bullitt Center consists of unregulated miscellaneous electrical loads (plug-loads), which are directly attributable to occupant behavior and equipment procurement choices. Approximately 16% of end-use energy is attributable to electric lighting which is also largely determined by occupant behavior. Key to the building’s energy efficiency is employment of lighting controls and daylighting strategies to minimize the lighting load. This paper uses measured energy use in a 330 m2 (3550 ft2) open office space in this building to inform occupant profiles that are then modified to create four scenarios to model the impact of behavior on lighting use. By using measured energy consumption and an energy model to simulate the energy performance of this space, this paper evaluates the potential energy savings based on different occupant behavior. This paper describes occupant behavior simulation methods and evaluates them using a robust dataset of 15 minute interval sub-metered energy consumption data. Lighting control strategies are compared via simulation results, in order to achieve the best match between occupant schedules, controls, and energy savings. Using these findings, we propose a simulation methodology that incorporates measured energy use data to generate occupant schedules and control schemes with the ultimate aim of using simulation results to evaluate energy saving measures that target occupant behavior.


lighting control ultra-low energy building occupant behavior building simulation energy consumption 


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This study was supported by the Ministry of Housing and Urban-Rural Development and the Ministry of Science & Technology of China, under the U.S.–China Clean Energy Research Center for Building Energy Efficiency (grant no. 2016YFE0102300-04). The authors wish to thank the Northwest Energy Efficiency Alliance (NEEA) for on-going support of the University of Washington Integrated Design Lab and the University of Washington Clean Energy Institute and Urban@UW for travel support to facilitate this collaboration.


  1. Bourgeois D, Reinhart C, Macdonald I (2006). Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control. Energy and Buildings, 38: 814–823.CrossRefGoogle Scholar
  2. Boyce PR (1980). Observations of the manual switching of lighting. Lighting Research & Technology, 12: 195–205.CrossRefGoogle Scholar
  3. CBECS (2012). Energy Usage Summary. Accessed 30 Nov 2016.Google Scholar
  4. Chang WK, Hong T (2013). Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lightingswitch data. Building Simulation, 6: 23–32.CrossRefGoogle Scholar
  5. Delvaeye R, Ryckaert W, Stroobant L, Hanselaer P, Klein R, Breesch H (2016). Analysis of energy savings of three daylight control systems in a school building by means of monitoring. Energy and Buildings, 127: 969–979.CrossRefGoogle Scholar
  6. Enmetric Systems (2016). Enmetric Hardware. Available at Scholar
  7. Hunt DRG, Cockram AH (1978). Field studies of the use of artificial lighting in offices. BRE current paper 47/78. Bricket Wood, UK: Building Research Establishment.Google Scholar
  8. Hunt DRG (1979). The use of artificial lighting in relation to daylight levels and occupancy. Building and Environment, 14: 21–33.CrossRefGoogle Scholar
  9. Hunt DRG (1980). Predicting artificial lighting use—A method based upon observed patterns of behavior. Lighting Research & Technology, 12: 7–14.CrossRefGoogle Scholar
  10. Gilbride M, Loveland J, Burpee H, Kriegh J, Meek C (2016). Occupant behavior driven energy savings at the Bullitt Center in Seattle, WA. In: Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA.Google Scholar
  11. Guo X, Tiller DK, Henze GP, Waters CE (2010). The performance of occupancy-based lighting control systems: A review. Lighting Research & Technology, 42: 415–431.CrossRefGoogle Scholar
  12. Jennings JD, Rubinstein FM, DiBartolomeo D, Blanc SL (2000). Comparison of controls options in private offices in an advanced lighting controls testbed. Journal of the Illuminating Engineering Society, 29: 39–60.CrossRefGoogle Scholar
  13. Mahdavi A, Mohammadi A, Kabir E, Lambeva L (2008). Occupants’ operation of lighting and shading systems in office buildings. Journal of Building Performance Simulation, 2008, 1: 57–65.CrossRefGoogle Scholar
  14. Maniccia D, Rutledge B, Rea MS, Morrow W (2013). Occupant use of manual lighting controls in private offices. Journal of the Illuminating Engineering Society, 28: 42–56.CrossRefGoogle Scholar
  15. Meek C, Gilbride M, Ojaama H, Norwood W (2015). The Bullitt Center experience: The light dynamic—Measured performance of lighting and daylight. In: Proceedings of the BEST4 Conference, Kansas City, USA.Google Scholar
  16. Moore T, Carter D J, Slater AI (2003). Long-term patterns of use of occupant controlled office lighting. Lighting Research & Technology, 35: 43–57.CrossRefGoogle Scholar
  17. New Buildings Institute (NBI) (2016). List of Zero Net Energy Buildings. Available at Accessed 31 Dec 2016.Google Scholar
  18. Reinhart CF, Voss K (2003). Monitoring manual control of electric lighting and blinds. Lighting Research & Technology, 35: 243–258.CrossRefGoogle Scholar
  19. Reinhart CF (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77: 15–28.CrossRefGoogle Scholar
  20. Sadeghi SA, Karava P, Konstantzos I, Tzempelikos A (2016). Occupant interactions with shading and lighting systems using different control interfaces: A pilot field study. Building and Environment, 97: 177–195.CrossRefGoogle Scholar
  21. Wang C, Yan D, Sun H, Jiang Y (2015). A generalized probabilistic formula relating occupant behavior to environmental conditions. Building and Environment, 95: 53–62.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Yun GY, Kim H, Kim JT (2012). Effects of occupancy and lighting use patterns on lighting energy consumption. Energy and Buildings, 46: 152–158.CrossRefGoogle Scholar
  24. Zhou X, Yan D, Hong T, Ren X (2015). Data analysis and stochastic modeling of lighting energy use in large office buildings in China. Energy and Buildings, 86: 275–287.CrossRefGoogle Scholar
  25. Zhou X, Yan D, Feng X, Deng G, Jian Y, Jiang Y (2016). Influence of household air-conditioning use modes on the energy performance of residential district cooling systems. Building Simulation, 9: 429–441.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Building Science, School of ArchitectureTsinghua UniversityBeijingChina
  2. 2.Integrated Design Lab, Department of Architecture, College of Built EnvironmentsUniversity of WashingtonSeattleUSA

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