Building Simulation

, Volume 10, Issue 6, pp 845–859 | Cite as

An agent-based model of building occupant behavior during load shedding

  • Handi Chandra Putra
  • Clinton J. AndrewsEmail author
  • Jennifer A. Senick
Research Article


Load shedding enjoys increasing popularity as a way to reduce power consumption in buildings during hours of peak demand on the electricity grid. This practice has well known cost saving and reliability benefits for the grid, and the contracts utilities sign with their “interruptible” customers often pass on substantial electricity cost savings to participants. Less well-studied are the impacts of load shedding on building occupants, hence this study investigates those impacts on occupant comfort and adaptive behaviors. It documents experience in two office buildings located near Philadelphia (USA) that vary in terms of controllability and the set of adaptive actions available to occupants. An agent-based model (ABM) framework generalizes the case-study insights in a “what-if” format to support operational decision making by building managers and tenants. The framework, implemented in EnergyPlus and NetLogo, simulates occupants that have heterogeneous thermal and lighting preferences. The simulated occupants pursue local adaptive actions such as adjusting clothing or using portable fans when central building controls are not responsive, and experience organizational constraints, including a corporate dress code and miscommunication with building managers. The model predicts occupant decisions to act fairly well but has limited ability to predict which specific adaptive actions occupants will select.


locus of control building energy modeling occupant behavior load shedding agent-based modeling 


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We thank colleagues who performed much of the fieldwork and preparatory modeling work on this project including MaryAnn Sorensen Allacci, Irina Feygina, Elizabeth Hewitt, Ishanie Niogi, and Ke Xu. Financial support was provided by U.S. Department of Energy award DE-EE0004261 and National science Foundation award AGS-1645786.

Supplementary material

12273_2017_384_MOESM1_ESM.pdf (662 kb)
An agent-based model of building occupant behavior during load shedding


  1. Andrews CJ, Yi D, Krogmann U, Senick JA, Wener RE (2011). Designing buildings for real occupants: An agent-based approach. IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans, 41: 1077–1091.CrossRefGoogle Scholar
  2. Azar E, Menassa CC (2012a). Agent-based modeling of occupants and their impact on energy use in commercial buildings. Journal of Computing in Civil Engineering, 26: 506–518.CrossRefGoogle Scholar
  3. Azar E, Menassa CC (2012b). A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy and Buildings, 55: 841–853.CrossRefGoogle Scholar
  4. Capozza A, D’Adamo C, Mauri G, Pievatolo A (2005). Load shedding and demand side management enhancements to improve the security of a national electrical system. In: Proceedings of 2005 IEEE Russia Power Tech, St. Petersburg, Russia, pp. 3267–3295.Google Scholar
  5. Cappers P, Goldman C, Kathan D (2010). Demand response in U.S. electricity markets: Empirical evidence. Energy, 35: 1526–1535.CrossRefGoogle Scholar
  6. Chen J, TaylorJE, Wei HH (2012). Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation. Energy and Buildings, 47: 515–524.CrossRefGoogle Scholar
  7. Chu J, Wang C, Chen J, Wang H (2009). Agent-based residential water use behavior simulation and policy implications: A case-study in Beijing city. Water Resources Management, 23: 3267–3295.CrossRefGoogle Scholar
  8. Cowart R (2016). Demand Response as a Power System Resource—Growing Experience in the US. Paper presented at the Forum for Energy Analysis, Warsaw, Poland. Available at http://www.raponline. org. Accessed 6 Jan 2017.Google Scholar
  9. de Wilde P (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in Construction, 41: 40–49.CrossRefGoogle Scholar
  10. EIA (2016). 2012 Commercial Buildings Energy Consumption Survey: Energy Usage Summary, Table 6: Electricity consumption by end use. Energy Information Administration, U.S. Department of Energy. Available at Accessed 6 Jan 2017.Google Scholar
  11. EnergyPlus (2009). Input/Output Reference: The Encyclopedic Reference to EnergyPlus Input and Output. The Board of Trustees of the University of Illinois and the Regents of the University of California through the Ernest Orlando Lawrence Berkeley National Laboratory. Available at Scholar
  12. Faranda R (2007). Load Shedding: A new proposal. IEEE Transactions on Power Systems, 22: 2086–2093.CrossRefGoogle Scholar
  13. Faruqui A, Hledik R, Palmer J (2012). Time-varying and dynamic rate design. Report 4 in Brattle Group Global Power Best Practice Series. Available at Accessed 6 Jan 6 2017.Google Scholar
  14. Feng X, Yan D, Hong T (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348–359.CrossRefGoogle Scholar
  15. Gulbinas R, Taylor JE (2014). Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings. Energy and Buildings, 84: 493–500.CrossRefGoogle Scholar
  16. Haldi F, Robinson D (2009). Interactions with window openings by office occupants. Building and Environment, 44: 2378–2395.CrossRefGoogle Scholar
  17. Hoes P, Hensen JLM, Loomans MGLC, de Vries B, Bourgeois D (2009). User behavior in whole building simulation. Energy and Buildings, 41: 295–302.CrossRefGoogle Scholar
  18. Hong T, D’Oca S, Turner WJN, Taylor-Lange SC (2015a). An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 92: 764–777.CrossRefGoogle Scholar
  19. Hong T, D’Oca S, Taylor-Lange SC, Turner WJN, Chen Y, Corgnati SP (2015b). 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.CrossRefGoogle Scholar
  20. Huang J (2007). Scorecards of the commercial building prototypes. LBNL Report, Berkeley, CA, USA.Google Scholar
  21. Kashif A, Le Binh X, Dugdale J, Ploix S (2011). Agent based framework to simulate inhabitants’behaviour in domestic settings for energy management. In: Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Rome, Italy.Google Scholar
  22. Langevin J, Wen J, Gurian PL (2014). Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors. Building and Environment, 88: 27–45.CrossRefGoogle Scholar
  23. Lee YS, Malkawi AM (2014). Simulating multiple occupant behaviors in buildings: An agent-based modeling approach. Energy and Buildings, 69: 407–416.CrossRefGoogle Scholar
  24. Li X, Wen J (2014). Review of building energy modeling for control and operation. Renewable and Sustainable Energy Reviews, 37: 517–537.CrossRefGoogle Scholar
  25. Lim Y, Kim H-M, Kinoshita T (2014). Distributed load-shedding system for agent-based autonomous microgrid operations. Energies, 7: 385–401.CrossRefGoogle Scholar
  26. Linkola L, Andrews CJ, Schuetze T (2013). An agent based model of household water use. Water, 5: 1082–1100.CrossRefGoogle Scholar
  27. Macal CM, North MJ (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4: 151–162.CrossRefGoogle Scholar
  28. Mahdavi A, Pröglhöf C (2009). Toward empirically-based models of people’s presence and actions in buildings. In: Proceedings of the 11th International IBPSA Building Simulation Conference, Glasgow, UK, pp. 537–544.Google Scholar
  29. Masoso OT, Grobler LJ (2009). The dark side of occupants’ behaviour on building energy use. Energy and Buildings, 42: 173–177.CrossRefGoogle Scholar
  30. Newsham GR, Bowker BG (2010). The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review. Energy Policy, 38: 3289–3296.CrossRefGoogle Scholar
  31. Nicol JF (2001). Characterizing occupant behavior in buildings: Towards a stochastic model of occupant use of windows, lights, blinds, heaters, and fans. In: Proceedings of the 7th International IBPSA Building Simulation Conference, Rio de Janerio, Brazil, pp. 1073–1078.Google Scholar
  32. O’Brien W, Gunay HB (2014). The contextual factors contributing to occupants’ adaptive comfort behaviors in offices: A review and proposed modeling framework. Building and Environment, 77: 77–87.CrossRefGoogle Scholar
  33. Ouyang J, Hokao K (2009). Energy-saving potential by improving occupants’ behaviour in urban residential sector in Hangzhou City, China. Energy and Buildings, 41: 711–720.CrossRefGoogle Scholar
  34. Rijal HB, Tuohy P, Humphreys MA, Nicol JF, Samuel A, Clarke J (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39: 823–836.CrossRefGoogle Scholar
  35. Robinson D, Wilke U, Haldi F (2011). Multi agent simulation of occupant’s presence and behaviour. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.Google Scholar
  36. Senick JA (2015). Why energy-saving measures in commercial office buildings fail: Deep versus shallow use structures. PhD Thesis, Rutgers University, USA.Google Scholar
  37. Senick JA, Wener RE, Feygina I, Sorensen AM, Andrews CJ (2013). Occupant Behavior in Response to Energy-Saving Retrofits and Operations. Prepared by the Center for Green Building at Rutgers University for the Energy Efficient Buildings Hub, Philadelphia, PA, USA.Google Scholar
  38. Seryak J, Kissock K (2000). Occupancy and behavioural affects on residential energy use. In: Proceedings of American Solar Energy Society (ASES) Annual Conference, Madison, WI, USA.Google Scholar
  39. Stluka P (2014). Control for energy-efficient buildings. In: Samad T, Annaswamy AM (eds), The Impact of Control Technology, 2snd edn. Available at Accessed 6 Jan 2017.Google Scholar
  40. Sun Y, Wang S, Xiao F, Gao D (2013). Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: A review. Energy Conversion and Management, 71: 101–114.CrossRefGoogle Scholar
  41. Tisue S, Wilensky U (2004). Netlogo: A simple environment for modeling complexity. In: Proceedings of International Conference on Complex Systems, Boston, USA, pp. 16–21.Google Scholar
  42. Toftum J, Andersen RV, Jensen KL (2009). Occupant performance and building energy consumption with different philosophies of determining acceptable thermal conditions. Building and Environment, 44: 2009–2016.CrossRefGoogle Scholar
  43. Tools of Change (2012). Cool Biz, Japan. Case study designated with Landmark status. Available at Accessed 6 Jan 2017.Google Scholar
  44. Wagner S, Xu K, Delgoshaei P (2014). Measurement and verification report for. Liberty Property Trust PECO-DOE Project Pilot Phase Buildings. Report prepared for the U.S. Department of Energy by the Consortium for Building Energy Innovation, Pennsylvania State University, State College, PA, USA.Google Scholar
  45. Wilensky U (1999). NetLogo Software. Available at Accessed 7 Jan 2017.Google Scholar
  46. 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.CrossRefGoogle Scholar
  47. Yun GY, Steemers K (2008). Time-dependent occupant behaviour models of window control in summer. Energy and Buildings, 43: 1471–1482.CrossRefGoogle Scholar
  48. Zhang D, Li S (2014). Agent-based modeling and simulation of competitive electric power markets. In: Proceedings of Power Systems Conference, Clemson, SC, USA. Available at Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Handi Chandra Putra
    • 1
  • Clinton J. Andrews
    • 1
    Email author
  • Jennifer A. Senick
    • 1
  1. 1.Rutgers Center for Green Building, E.J. Bloustein School of Planning and Public Policy, RutgersThe State University of New JerseyNew BrunswickUSA

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