IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v195y2020ics0360544220300888.html
   My bibliography  Save this article

Probabilistic window opening model considering occupant behavior diversity: A data-driven case study of Canadian residential buildings

Author

Listed:
  • Rouleau, Jean
  • Gosselin, Louis

Abstract

It was found from monitored data from eight dwellings in a case study building in Quebec City (Canada) that there are clear differences in the window opening behavior between different households. This paper aims to develop from data a probabilistic window opening model that accounts for occupant behavior. Logit regression is employed to predict the state (opened/closed) of windows according to indoor and outdoor temperatures environmental and temporal parameters. To replicate the diversity of behavior, normal distribution functions applied to the logit regression coefficients are used so that simulated occupants respond differently to environmental stimuli. It was found that the model offers good prediction for the monitoring by only using the outdoor and indoor temperatures as predictors. The proposed methodology was tested by simulating 10,000 times a full validation year of the case study building and comparing the results with measured data. The agreement was good. The model overestimated slightly the total frequency of window opening in the dwellings and the number of window changes-of-state. A vast range of window opening behavior was generated by the model, showing its ability to reproduce both the aggregated window opening behavior and the diversity of behaviors of the case study building.

Suggested Citation

  • Rouleau, Jean & Gosselin, Louis, 2020. "Probabilistic window opening model considering occupant behavior diversity: A data-driven case study of Canadian residential buildings," Energy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220300888
    DOI: 10.1016/j.energy.2020.116981
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220300888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.116981?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. van Raaij, W. Fred & Verhallen, Theo M. M., 1983. "Patterns of residential energy behavior," Journal of Economic Psychology, Elsevier, vol. 4(1-2), pages 85-106, October.
    2. Li, Nan & Li, Juncheng & Fan, Ruijuan & Jia, Hongyuan, 2015. "Probability of occupant operation of windows during transition seasons in office buildings," Renewable Energy, Elsevier, vol. 73(C), pages 84-91.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jeong, Bongchan & Kim, Jungsoo & Chen, Dong & de Dear, Richard, 2023. "Development of a probabilistic behavioural model creating diverse A/C operation patterns of households," Energy, Elsevier, vol. 263(PB).
    2. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    3. Beilei Qin & Xi Xu & Takashi Asawa & Lulu Zhang, 2022. "Experimental and Numerical Analysis on Effect of Passive Cooling Methods on an Indoor Thermal Environment Having Floor-Level Windows," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    4. Manxuan Xiao & Wu Deng & Haipeng Ma & Jinshun Wu & Tongyu Zhou & Jinsong Zhu & Yasha Wang & Song Pan, 2024. "Influence of Subjective Factors on Window Use in Maternity Hospitals in Spring," Sustainability, MDPI, vol. 16(22), pages 1-29, November.

    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. Halil Alibaba, 2016. "Determination of Optimum Window to External Wall Ratio for Offices in a Hot and Humid Climate," Sustainability, MDPI, vol. 8(2), pages 1-21, February.
    2. Małgorzata Fedorczak-Cisak & Katarzyna Nowak & Marcin Furtak, 2019. "Analysis of the Effect of Using External Venetian Blinds on the Thermal Comfort of Users of Highly Glazed Office Rooms in a Transition Season of Temperate Climate—Case Study," Energies, MDPI, vol. 13(1), pages 1-18, December.
    3. Véronique Vasseur & Anne-Francoise Marique, 2019. "Households’ Willingness to Adopt Technological and Behavioral Energy Savings Measures: An Empirical Study in The Netherlands," Energies, MDPI, vol. 12(22), pages 1-25, November.
    4. Petricli, Gulcan & Inkaya, Tulin & Gokay Emel, Gul, 2024. "Identifying green citizen typologies by mining household-level survey data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    5. Mullaly, Cathy, 1998. "Home energy use behaviour: a necessary component of successful local government home energy conservation (LGHEC) programs," Energy Policy, Elsevier, vol. 26(14), pages 1041-1052, December.
    6. Bindu Shrestha & Sudarshan R. Tiwari & Sushil B. Bajracharya & Martina M. Keitsch & Hom B. Rijal, 2021. "Review on the Importance of Gender Perspective in Household Energy-Saving Behavior and Energy Transition for Sustainability," Energies, MDPI, vol. 14(22), pages 1-18, November.
    7. M. Esteban Muñoz H. & Irene Peters, 2014. "Constructing an Urban Microsimulation Model to Assess the Influence of Demographics on Heat Consumption," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 127-157.
    8. Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019. "Energy saving estimation for plug and lighting load using occupancy analysis," Renewable Energy, Elsevier, vol. 143(C), pages 1143-1161.
    9. Wang, Qinpeng & Augenbroe, Godfried & Kim, Ji-Hyun & Gu, Li, 2016. "Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings," Applied Energy, Elsevier, vol. 174(C), pages 166-180.
    10. Reihaneh Aram & Halil Zafer Alibaba, 2019. "Thermal Comfort and Energy Performance of Atrium in Mediterranean Climate," Sustainability, MDPI, vol. 11(4), pages 1-29, February.
    11. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    12. Elisabeth Gsottbauer & Jeroen Bergh, 2011. "Environmental Policy Theory Given Bounded Rationality and Other-regarding Preferences," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 49(2), pages 263-304, June.
    13. Jia, Mengda & Srinivasan, Ravi S. & Raheem, Adeeba A., 2017. "From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 525-540.
    14. Luca Caneparo, 2020. "Financing the (Environmental) Quality of Cities with Energy Efficiency Investments," Sustainability, MDPI, vol. 12(21), pages 1-25, October.
    15. Elisha R. Frederiks & Karen Stenner & Elizabeth V. Hobman, 2015. "The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review," Energies, MDPI, vol. 8(1), pages 1-37, January.
    16. Circella, Giovanni & Johnston, Robert & Holguin, Andrew & Lehmer, Eric & Wang, Yang & McCoy, Michael, 2013. "Updating the PECAS Modeling Framework to Include Energy Use Data for Buildings," Institute of Transportation Studies, Working Paper Series qt8jr035gh, Institute of Transportation Studies, UC Davis.
    17. Hong, Juwon & Kang, Hyuna & Hong, Taehoon, 2020. "Oversampling-based prediction of environmental complaints related to construction projects with imbalanced empirical-data learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    18. Valentine, Scott & Sovacool, Benjamin K. & Matsuura, Masahiro, 2011. "Empowered? Evaluating Japan's national energy strategy under the DPJ administration," Energy Policy, Elsevier, vol. 39(3), pages 1865-1876, March.
    19. Manxuan Xiao & Wu Deng & Haipeng Ma & Jinshun Wu & Tongyu Zhou & Jinsong Zhu & Yasha Wang & Song Pan, 2024. "Influence of Subjective Factors on Window Use in Maternity Hospitals in Spring," Sustainability, MDPI, vol. 16(22), pages 1-29, November.
    20. Zhuo Jia & Song Pan & Haowei Yu & Yiqiao Liu & Shen Wei & Mingyuan Qin & Li Chang & Ying Cui, 2023. "Modeling Occupant Window Behavior in Hospitals—A Case Study in a Maternity Hospital in Beijing, China," Sustainability, MDPI, vol. 15(11), pages 1-29, May.

    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:energy:v:195:y:2020:i:c:s0360544220300888. 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.journals.elsevier.com/energy .

    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.