Journal Home > Volume 11 , Issue 4

Despite the research advances and demonstrated benefit of occupant modelling and simulation in recent years, its uptake in building simulation practice has been relatively slow. One of the underlying causes of this issue is limited occupant-related features of building performance simulation (BPS) tools. To this end, we present a detailed breakdown of occupant-related features and compare them between common BPS tools. Based on the outcomes of a stakeholder workshop, and an international survey that focused on occupant modelling, we provide detailed recommendations to improve occupant-related features in BPS tools. We finally present a case study demonstrating the suggested occupant-related features to apply multiple occupancy assumptions, and integrate occupant behaviour models from the literature. Results are presented as part of a non-functional mock-up graphical user interface (GUI) to demonstrate potential features of BPS tools given the suggested occupant-related improvements. These suggested improvements for BPS tools would enable users to quickly assess proposed designs’ sensitivity to different occupancy scenarios, and ultimately increase the robustness of their final designs. The presented recommendations are relevant to practitioners, researchers, and BPS tool developers as part of the efforts to increase the uptake of detailed occupant modelling in building simulation practice.


menu
Abstract
Full text
Outline
About this article

Improving occupant-related features in building performance simulation tools

Show Author's information Mohamed M. Ouf( )William O’BrienH. Burak Gunay
Department of Civil Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada

Abstract

Despite the research advances and demonstrated benefit of occupant modelling and simulation in recent years, its uptake in building simulation practice has been relatively slow. One of the underlying causes of this issue is limited occupant-related features of building performance simulation (BPS) tools. To this end, we present a detailed breakdown of occupant-related features and compare them between common BPS tools. Based on the outcomes of a stakeholder workshop, and an international survey that focused on occupant modelling, we provide detailed recommendations to improve occupant-related features in BPS tools. We finally present a case study demonstrating the suggested occupant-related features to apply multiple occupancy assumptions, and integrate occupant behaviour models from the literature. Results are presented as part of a non-functional mock-up graphical user interface (GUI) to demonstrate potential features of BPS tools given the suggested occupant-related improvements. These suggested improvements for BPS tools would enable users to quickly assess proposed designs’ sensitivity to different occupancy scenarios, and ultimately increase the robustness of their final designs. The presented recommendations are relevant to practitioners, researchers, and BPS tool developers as part of the efforts to increase the uptake of detailed occupant modelling in building simulation practice.

Keywords: building performance simulation, occupant behaviour, occupancy, building simulation software tools, occupant modelling

References(43)

ASHRAE (2013). ASHRAE Standard 90.1-2013, Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta: American Society of Heating Refrigerating and Air-Conditioning Engineers.
ASHRAE (2017). ASHRAE Standard 55–2017, Thermal Environmental Conditions for Human Occupancy. Atlanta: American Society of Heating Refrigerating and Air-Conditioning Engineers.
IE Bennet, W O’Brien, HB Gunay (2014). Effect of window blind use in residential buildings: Observation and simulation study. In: Proceedings of eSim Conference, Ottawa, Canada.
IE Bennet, W O’Brien (2017). Office building plug and light loads: Comparison of a multi-tenant office tower to conventional assumptions. Energy and Buildings, 153: 461–475.
L Berglund (1978). Mathematical models for predicting the thermal comfort response of building occupants. ASHRAE Transactions, 84(1): 735–749.
S Carlucci (2013). Thermal Comfort Assessment of Buildings. London: Springer.
DOI
JA Clarke, JLM Hensen (2015). Integrated building performance simulation: Progress, prospects and requirements. Building and Environment, 91: 294–306.
LL Constantine, LAD Lockwood (2002). Usage-centered engineering for Web applications. IEEE Software, 19(2): 42–50.
A Cowie, T Hong, X Feng, Q Darakdjian (2017). Usefulness of the obFMU module examined through a review of occupant modelling functionality in building performance simulation programs. In: Proceedings of IBPSA International Building Simulation Conference, San Francisco, USA.
PC da Silva, V Leal, M Andersen (2015). Occupants’ behaviour in energy simulation tools: lessons from a field monitoring campaign regarding lighting and shading control. Journal of Building Performance Simulation, 8: 338–358.
S D’Oca, T Hong (2014). A data-mining approach to discover patterns of window opening and closing behavior in offices. Building and Environment, 82: 726–739.
V Djunaedy, K Wymelenberg, B Acker, H Thimmanna (2011). Rightsizing: Using simulation tools to solve the problem of oversizing. In: Proceedings of IBPSA International Building Simulation Conference, Sydney, Australia.
X Feng, D Yan, T Hong (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348–359.
ZM Gill, MJ Tierney, IM Pegg, N Allan (2010). Low-energy dwellings: The contribution of behaviours to actual performance. Building Research and Information, 38: 491–508.
HB Gunay, W O’Brien, I Beausoleil-Morrison (2013). A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices. Building and Environment, 70: 31–47.
HB Gunay, W O’Brien, I Beausoleil-Morrison (2016). Implementation and comparison of existing occupant behaviour models in EnergyPlus. Journal of Building Performance Simulation, 9: 567–588.
HB Gunay, W O’Brien, I Beausoleil-Morrison, J Bursill (2017). Development and implementation of a thermostat learning algorithm. Science and Technology for the Built Environment, 4731: 1–14.
F Haldi, D Robinson (2010). Adaptive actions on shading devices in response to local visual stimuli. Journal of Building Performance Simulation, 3: 135–153.
F Haldi, D Robinson (2011). The impact of occupants’ behaviour on building energy demand. Journal of Building Performance Simulation, 4: 323–338.
D Harris, C Higgins (2013). Methodology for Reporting Commercial Office Plug Load Energy Use. New Buildings Institute. Available at https://newbuildings.org/wp-content/uploads/2015/11/PlugLoadMetricsReportingGuide_CaseStudy1.pdf.
T Hong, S D’Oca, WJN Turner, SC Taylor-Lange (2015a). An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework. Building and Environment, 92: 764–777.
T Hong, S D’Oca, SC Taylor-Lange, WJN Turner, Y Chen, SP Corgnati (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.
T Hong, H Sun, Y Chen, SC Taylor-Lange, D Yan (2016). An occupant behavior modeling tool for co-simulation. Energy and Buildings, 117: 272–281.
T Hong, Y Chen, Z Belafi, S D’Oca (2018). Occupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs. Building Simulation, 11: 1–14.
AJM Lindner, S Park, M Mitterhofer (2017). Determination of requirements on occupant behavior models for the use in building performance simulations. Building Simulation, 10: 861–874.
NRC (2015). National Energy Code of Canada for Buildings. Canadian Commission on Building and Fire Codes, Ottawa: National Research Council of Canada.
T Nghiem (2010). MLE+: A Matlab-EnergyPlus Co-simulation Interface. Philadelphia: University of Pennsylvania.
LK Norford, RH Socolow, ES Hsieh, GV Spadaro (1994). Two-to-one discrepancy between measured and predicted performance of a “low-energy” office building: insights from a reconciliation based on the DOE-2 model. Energy and Buildings, 21: 121–131.
W O’Brien, HB Gunay (2014). The contextual factors contributing to occupants’ adaptive comfort behaviors in offices—A review and proposed modeling framework. Building and Environment, 77: 77–88.
W O’Brien, HB Gunay (2015). Mitigating office performance uncertainty of occupant use of window blinds and lighting using robust design. Building Simulation, 8: 621–636.
W O’Brien, I Bennet (2016). Simulation-based evaluation of high-rise residential building thermal resilience. ASHRAE Transactions, 122(1): 455–468.
W O’Brien, I Gaetani, S Carlucci, P-J Hoes, JLM Hensen (2017a). On occupant-centric building performance metrics. Building and Environment, 122: 373–385.
W O’Brien, I Gaetani, S Gilani, S Carlucci, P-J Hoes, J Hensen (2017b). International survey on current occupant modelling approaches in building performance simulation. Journal of Building Performance Simulation, 10: 653–671.
W O’Brien, HB Gunay, F Tahmasebi, A Mahdavi (2017c). A preliminary study of representing the inter-occupant diversity in occupant modelling. Journal of Building Performance Simulation, 10: 509–526.
J Page, D Robinson, N Morel, JL Scartezzini (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40: 83–98.
CF Reinhart (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77: 15–28.
A Roth, D Goldwasser, A Parker (2016). There’s a measure for that! Energy and Buildings, 117: 321–331.
S Schiavon, KH Lee (2013). Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures. Building and Environment, 59: 250–260.
D Wang, CC Federspiel, F Rubinstein (2005). Modeling occupancy in single person offices. Energy and Buildings, 37: 121–126.
C Wang, D Yan, Y Jiang (2011). A novel approach for building occupancy simulation. Building Simulation, 4: 149–167.
H Wang, Z Zhai (2016). Advances in building simulation and computational techniques: A review between 1987 and 2014. Energy and Buildings, 128: 319–335.
M Wetter (2008). A modular building controls virtual test bed for the integrations of heterogeneous systems. In: Proceedings of SimBuild. Berkeley, CA, USA.
D Yan, W O’Brien, T Hong, X Feng, HB Gunay, F Tahmasebi, A Mahdavi (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264–278.
Publication history
Copyright
Acknowledgements

Publication history

Received: 28 December 2017
Revised: 23 February 2018
Accepted: 19 March 2018
Published: 02 April 2018
Issue date: August 2018

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Acknowledgements

This research was supported by Natural Resources Canada (NRCan), under the Clean Energy Innovation (CEI) component of the Energy Innovation Program (EIP). The workshop was funded by Natural Science and Engineering Research Council (NSERC). The authors would also like to thank the workshop participants for devoting over a day of their time to travel to Ottawa to provide valuable input to workshop.

Return