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On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation

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  • Advances in Modeling and Simulation Tools
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Abstract

Occupant behavior (OB) is one of the significant sources of uncertainty in building performance simulation. While OB modeling has received increased attention in the past decade, research on the degree of granularity or level of detail (LoD) required for representing occupants is still in the nascent stages. This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment. A medium-sized prototype office building located in Chicago, Illinois is used as the simulation case study. Ten occupant-centric attributes are adopted to develop the LoDs for OB representation. We first demonstrate the different modeling approaches required for simulating the three fidelity levels. Later, we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages. This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

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References

  • Cabeza LF, Bai Q, Bertoldi P, et al. (2022). Buildings. In: Shukla PR, Skea J, Slade R, et al. (Eds.), IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Chandra Putra H, Hong T, Andrews C (2021). An ontology to represent synthetic building occupant characteristics and behavior. Automation in Construction, 125: 103621.

    Article  Google Scholar 

  • Chen Y, Hong T, Luo X (2018). An agent-based stochastic occupancy simulator. Building Simulation, 11: 37–49.

    Article  Google Scholar 

  • Department of Energy (2019). Commercial Building Prototype Model. Available at https://www.energycodes.gov/development/commercial/prototype_models.

  • Ding Y, Han S, Tian Z, et al. (2022). Review on occupancy detection and prediction in building simulation. Building Simulation, 15: 333–356.

    Article  Google Scholar 

  • Dong B, Liu Y, Mu W, et al. (2022). A global building occupant behavior database. Scientific Data, 9: 369.

    Article  Google Scholar 

  • Feng X, Yan D, Wang C (2017). On the simulation repetition and temporal discretization of stochastic occupant behaviour models in building performance simulation. Journal of Building Performance Simulation, 10: 612–624.

    Article  Google Scholar 

  • Ferrando M, Ferroni S, Pelle M, et al. (2022). UBEM’s archetypes improvement via data-driven occupant-related schedules randomly distributed and their impact assessment. Sustainable Cities and Society, 87: 104164.

    Article  Google Scholar 

  • Földváry Ličina V, Cheung T, Zhang H, et al. (2018). Development of the ASHRAE global thermal comfort database II. Building and Environment, 142: 502–512.

    Article  Google Scholar 

  • Gaetani I, Hoes PJ, Hensen JLM (2017). On the sensitivity to different aspects of occupant behaviour for selecting the appropriate modelling complexity in building performance predictions. Journal of Building Performance Simulation, 10: 601–611.

    Article  Google Scholar 

  • Grimm V, Berger U, DeAngelis DL, et al. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221: 2760–2768.

    Article  Google Scholar 

  • Gunay HB, O’Brien W, Beausoleil-Morrison I, et al. (2017). Development and implementation of an adaptive lighting and blinds control algorithm. Building and Environment, 113: 185–199.

    Article  Google Scholar 

  • Hu S, Zhang Y, Yang Z, et al. (2022). Challenges and opportunities for carbon neutrality in China’s building sector—modelling and data. Building Simulation, 15: 1899–1921.

    Article  Google Scholar 

  • IEA (2021). Do we need to change our behaviour to reach net zero by 2050? Available at https://www.iea.org/articles/do-we-need-to-change-our-behaviour-to-reach-net-zero-by-2050.

  • Jaxa-Rozen M, Kwakkel JH (2018). PyNetLogo: Linking NetLogo with Python. Journal of Artificial Societies and Social Simulation, 21(2): 4.

    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 

  • Li D, Menassa CC, Karatas A (2017). Energy use behaviors in buildings: Towards an integrated conceptual framework. Energy Research & Social Science, 23: 97–112.

    Article  Google Scholar 

  • Li D, Xu X, Chen CF, et al. (2019a). Understanding energy-saving behaviors in the American workplace: A unified theory of motivation, opportunity, and ability. Energy Research & Social Science, 51: 198–209.

    Article  Google Scholar 

  • Li J, Yu Z, Haghighat F, et al. (2019b). Development and improvement of occupant behavior models towards realistic building performance simulation: A review. Sustainable Cities and Society, 50: 101685.

    Article  Google Scholar 

  • Luo X, Lam KP, Chen Y, et al. (2017). Performance evaluation of an agent-based occupancy simulation model. Building and Environment, 115: 42–53.

    Article  Google Scholar 

  • Mahdavi A, Tahmasebi F (2016). The deployment-dependence of occupancy-related models in building performance simulation. Energy and Buildings, 117: 313–320.

    Article  Google Scholar 

  • Malik J, Azar E, Mahdavi A, et al. (2022a). A level-of-details framework for representing occupant behavior in agent-based models. Automation in Construction, 139: 104290.

    Article  Google Scholar 

  • Malik J, Mahdavi A, Azar E, et al. (2022b). Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings. Building and Environment, 217: 109016.

    Article  Google Scholar 

  • Michie S, van Stralen MM, West R (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6: 42.

    Article  Google Scholar 

  • Nouidui TS, Wetter M (2014). Tool coupling for the design and operation of building energy and control systems based on the Functional Mock-up Interface standard. In: Proceedings of the 10th International Modelica Conference, Lund, Sweden.

  • O’Brien W, Wagner A, Schweiker M, et al. (2020). Introducing IEA EBC annex 79: Key challenges and opportunities in the field of occupant-centric building design and operation. Building and Environment, 178: 106738.

    Article  Google Scholar 

  • Pang Z, Chen Y, Zhang J, et al. (2020). Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates. Applied Energy, 279: 115727.

    Article  Google Scholar 

  • PyFMI 2.9 (2021). A Package for Working with Dynamic Models Compliant with the Functional Mock-Up Interface Standard.

  • Sun K, Hong T (2017). A simulation approach to estimate energy savings potential of occupant behavior measures. Energy and Buildings, 136: 43–62.

    Article  Google Scholar 

  • Sutter Y, Dumortier D, Fontoynont M (2006). The use of shading systems in VDU task offices: A pilot study. Energy and Buildings, 38: 780–789.

    Article  Google Scholar 

  • Tahmasebi F, Mahdavi A (2017). The sensitivity of building performance simulation results to the choice of occupants’ presence models: a case study. Journal of Building Performance Simulation, 10: 625–635.

    Article  Google Scholar 

  • Tverskoi D, Xu X, Nelson H, et al. (2021). Energy saving at work: understanding the roles of normative values and perceived benefits and costs in single-person and shared offices in the United States. Energy Research & Social Science, 79: 102173.

    Article  Google Scholar 

  • Wang Z, Hong T, Piette MA (2019). Predicting plug loads with occupant count data through a deep learning approach. Energy, 181: 29–42.

    Article  Google Scholar 

  • Wang Z, Hong T (2020). Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States. Renewable and Sustainable Energy Reviews, 119: 109593.

    Article  Google Scholar 

  • Wetter M (2022). FMU Export of EnergyPlus.

  • Xu X, Chen CF, Li D, et al. (2020). Energy saving at work: Exploring the role of social norms, perceived control and ascribed responsibility in different office layouts. Frontiers in Built Environment, 6: 16.

    Article  Google Scholar 

  • Yan D, O–Brien W, Hong T, et al. (2015). Occupant behavior modeling for building performance simulation: current state and future challenges. Energy and Buildings, 107: 264–278.

    Article  Google Scholar 

  • Yan D, Hong T, Dong B, et al. (2017). IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings. Energy and Buildings, 156: 258–270.

    Article  Google Scholar 

  • Yan D, Zhou X, An J, et al. (2022). DeST 3.0: A new-generation building performance simulation platform. Building Simulation, 15: 1849–1868.

    Article  Google Scholar 

  • Yang T, Bandyopadhyay A, O’Neill Z, et al. (2022). From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control. Building Simulation, 15: 913–932.

    Article  Google Scholar 

  • Yoshino H, Hong T, Nord N (2017). IEA EBC Annex 53: Total energy use in buildings—Analysis and evaluation methods. Energy and Buildings, 152: 124–136.

    Article  Google Scholar 

  • Yun GY, Steemers K (2008). Time-dependent occupant behaviour models of window control in summer. Building and Environment, 43: 1471–1482.

    Article  Google Scholar 

  • Zhou X, Lu Y, Hu S, et al. (2023). New perspectives on temporal changes in occupancy characteristics of residential buildings. Journal of Building Engineering, 64: 105590.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the United States Department of Energy, under Contract No. DE-AC02-05CH11231. The authors would like to thank Dr. Ardeshir Mahdavi of Graz University of Technology, Austria, Dr. Elie Azar of Carleton University, Canada and Christiane Berger of Aalborg University, Denmark for their constructive feedback on the case study design. Authors benefited from participation and discussion in the project (2018–2023) Annex 79, Occupant-centric building design and operation, under the International Energy Agency’s Energy in Buildings and Communities Programme.

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All authors contributed to the study conception and design. Material preparation, simulation runs, and analysis were performed by Jeetika Malik and Handi Chandra Putra. The first draft of the manuscript was written by Jeetika Malik, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jeetika Malik.

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The authors have no competing interests to declare that are relevant to the content of this article.

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On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation

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Malik, J., Putra, H.C., Sun, K. et al. On the applicability of various levels of detail for occupant behavior representation and modeling in building performance simulation. Build. Simul. 16, 1481–1498 (2023). https://doi.org/10.1007/s12273-023-1034-0

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  • DOI: https://doi.org/10.1007/s12273-023-1034-0

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