IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v185y2023ics136403212300415x.html
   My bibliography  Save this article

State-of-the-art review of occupant behavior modeling and implementation in building performance simulation

Author

Listed:
  • Ahmed, Omar
  • Sezer, Nurettin
  • Ouf, Mohamed
  • Wang, Liangzhu (Leon)
  • Hassan, Ibrahim Galal

Abstract

Occupant Behavior (OB) is one of the major drivers of building energy consumption. However, OB is usually oversimplified in Building Performance Simulation (BPS), resulting in a significant performance gap between actual and simulated building energy use. Thus, understanding the true nature of OB and its accurate representation within BPS is crucial. Despite the existence of many review articles that focus on several aspects of OB, the vast majority of reviews are centered on a specific aspect of OB modeling, scattering the main findings among various studies. The literature still lacks a comprehensive review that compiles and analyzes the recent studies on each stage of OB such as data collection and analysis, modeling, integration of OB models into BPS, validation, and presentation of the data in a suitable format. To this end, the present review summarizes, compiles, and analyzes the recent literature on every aspect of OB in BPS, and presents an up-to-date evaluation of the multiple facets of OB modeling in BPS. It aims to present the development and implementation steps of the OB model within BPS tools. A general outline characterizing the recommended workflow for modeling OB in BPS is described. A brief categorization of data collection methods used in OB modeling is presented. Common quantitative OB modeling approaches in BPS i.e., Stochastic, Statistical, Data mining, and Agent-based methods, are elucidated. The main applications, advantages, and limitations of each model are discussed. The available literature on the influence of different OB patterns and occupants’ interaction with building systems, such as cooling, lighting, shading, and appliances, on building energy performance is evaluated. In brief, this study provides an up-to-date review of OB in BPS, offering valuable insights to both academic researchers and industrial professionals to aid them in choosing and adopting correct strategies to accurately model OB and incorporate it into available BPS tools.

Suggested Citation

  • Ahmed, Omar & Sezer, Nurettin & Ouf, Mohamed & Wang, Liangzhu (Leon) & Hassan, Ibrahim Galal, 2023. "State-of-the-art review of occupant behavior modeling and implementation in building performance simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:rensus:v:185:y:2023:i:c:s136403212300415x
    DOI: 10.1016/j.rser.2023.113558
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S136403212300415X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.113558?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    2. Hamed Nabizadeh Rafsanjani & Changbum R. Ahn & Mahmoud Alahmad, 2015. "A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings," Energies, MDPI, vol. 8(10), pages 1-34, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arkar, C. & Žižak, T. & Domjan, S. & Medved, S., 2020. "Dynamic parametric models for the holistic evaluation of semi-transparent photovoltaic/thermal façade with latent storage inserts," Applied Energy, Elsevier, vol. 280(C).
    2. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    3. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    4. Suzana Domjan & Sašo Medved & Boštjan Černe & Ciril Arkar, 2019. "Fast Modelling of nZEB Metrics of Office Buildings Built with Advanced Glass and BIPV Facade Structures," Energies, MDPI, vol. 12(16), pages 1-18, August.
    5. Cristina Brunelli & Francesco Castellani & Alberto Garinei & Lorenzo Biondi & Marcello Marconi, 2016. "A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings," Energies, MDPI, vol. 9(11), pages 1-15, November.
    6. Ghahramani, Ali & Pantelic, Jovan & Lindberg, Casey & Mehl, Matthias & Srinivasan, Karthik & Gilligan, Brian & Arens, Edward, 2018. "Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems," Applied Energy, Elsevier, vol. 230(C), pages 42-51.
    7. Fabrizio M. Amoruso & Udo Dietrich & Thorsten Schuetze, 2019. "Indoor Thermal Comfort Improvement through the Integrated BIM-Parametric Workflow-Based Sustainable Renovation of an Exemplary Apartment in Seoul, Korea," Sustainability, MDPI, vol. 11(14), pages 1-31, July.
    8. Byung-Ki Jeon & Eui-Jong Kim, 2021. "LSTM-Based Model Predictive Control for Optimal Temperature Set-Point Planning," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
    9. Ana Ogando & Natalia Cid & Marta Fernández, 2017. "Energy Modelling and Automated Calibrations of Ancient Building Simulations: A Case Study of a School in the Northwest of Spain," Energies, MDPI, vol. 10(6), pages 1-17, June.
    10. Waqas Ahmed Mahar & Griet Verbeeck & Manoj Kumar Singh & Shady Attia, 2019. "An Investigation of Thermal Comfort of Houses in Dry and Semi-Arid Climates of Quetta, Pakistan," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    11. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    12. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    13. Monika Zielińska-Sitkiewicz & Mariola Chrzanowska & Konrad Furmańczyk & Kacper Paczutkowski, 2021. "Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks," Energies, MDPI, vol. 14(20), pages 1-21, October.
    14. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
    15. Calama-González, Carmen María & Symonds, Phil & Petrou, Giorgos & Suárez, Rafael & León-Rodríguez, Ángel Luis, 2021. "Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring," Applied Energy, Elsevier, vol. 282(PA).
    16. Sukjoon Oh & Juan-Carlos Baltazar & Jeff S. Haberl, 2022. "Assessment of the Impact of Using a Smart Thermostat and Smart Meter Data on a Whole-Building Energy Simulation," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    17. Ghahramani, Ali & Castro, Guillermo & Karvigh, Simin Ahmadi & Becerik-Gerber, Burcin, 2018. "Towards unsupervised learning of thermal comfort using infrared thermography," Applied Energy, Elsevier, vol. 211(C), pages 41-49.
    18. Vering, Christian & Göbel, Stephan & Klebig, Tim & Will, Florian & Horst, Janik & Wüllhorst, Fabian & Nürenberg, Markus & Mehrfeld, Philipp & Müller, Dirk, 2024. "Towards a defossilized building sector with field tests in the lab: Review, development, and evaluation," Applied Energy, Elsevier, vol. 365(C).
    19. Jessika Steen Englund & Mathias Cehlin & Jan Akander & Bahram Moshfegh, 2020. "Measured and Simulated Energy Use in a Secondary School Building in Sweden—A Case Study of Validation, Airing, and Occupancy Behaviour," Energies, MDPI, vol. 13(9), pages 1-22, May.
    20. Massimiliano Manfren & Benedetto Nastasi, 2020. "Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings," Energies, MDPI, vol. 13(3), pages 1-14, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:185:y:2023:i:c:s136403212300415x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.