IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v105y2013icp358-368.html
   My bibliography  Save this article

Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption

Author

Listed:
  • Chen, Jiayu
  • Jain, Rishee K.
  • Taylor, John E.

Abstract

Recent research has shown that providing building occupants with eco-feedback regarding their own energy consumption and the consumption of others in their peer network can lead to substantial energy savings. While empirical eco-feedback studies have provided valuable insights into the dynamics of energy consumption behavior and building occupant peer networks, such studies have faced challenges in examining consumption behavior in larger and more complex peer networks. Computer simulation and random network models offer a solution to this scalability issue, but current random network models are limited in their ability to mimic real world building occupant networks. In this paper, we propose a refined random network model, the Block Configuration Model, and utilize it in an agent-based energy consumption simulation. Results indicate that the Block Configuration Model is more accurate than conventional models when compared to empirical data from three different eco-feedback experiments. The Block Configuration Model advances our understanding of the dynamics of occupant energy consumption and provides a tool to reduce energy consumption and associated emissions.

Suggested Citation

  • Chen, Jiayu & Jain, Rishee K. & Taylor, John E., 2013. "Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption," Applied Energy, Elsevier, vol. 105(C), pages 358-368.
  • Handle: RePEc:eee:appene:v:105:y:2013:i:c:p:358-368
    DOI: 10.1016/j.apenergy.2012.12.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261912009270
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2012.12.036?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. Negrão, Cezar O.R. & Hermes, Christian J.L., 2011. "Energy and cost savings in household refrigerating appliances: A simulation-based design approach," Applied Energy, Elsevier, vol. 88(9), pages 3051-3060.
    2. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    3. Vassileva, Iana & Wallin, Fredrik & Dahlquist, Erik, 2012. "Analytical comparison between electricity consumption and behavioral characteristics of Swedish households in rented apartments," Applied Energy, Elsevier, vol. 90(1), pages 182-188.
    4. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
    5. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    6. Hermes, Christian J.L. & Melo, Cláudio & Knabben, Fernando T. & Gonçalves, Joaquim M., 2009. "Prediction of the energy consumption of household refrigerators and freezers via steady-state simulation," Applied Energy, Elsevier, vol. 86(7-8), pages 1311-1319, July.
    7. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    8. Saul Amorim & Jean-Pierre Barthélemy & Celso Ribeiro, 1992. "Clustering and clique partitioning: Simulated annealing and tabu search approaches," Journal of Classification, Springer;The Classification Society, vol. 9(1), pages 17-41, January.
    9. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    10. Olofsson, Thomas & Mahlia, T.M.I., 2012. "Modeling and simulation of the energy use in an occupied residential building in cold climate," Applied Energy, Elsevier, vol. 91(1), pages 432-438.
    11. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    12. Noah J. Goldstein & Robert B. Cialdini & Vladas Griskevicius, 2008. "A Room with a Viewpoint: Using Social Norms to Motivate Environmental Conservation in Hotels," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(3), pages 472-482, March.
    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. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Wang, Wei & Chen, Jiayu & Huang, Gongsheng & Lu, Yujie, 2017. "Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution," Applied Energy, Elsevier, vol. 207(C), pages 305-323.
    4. Batalla-Bejerano, Joan & Trujillo-Baute, Elisa & Villa-Arrieta, Manuel, 2020. "Smart meters and consumer behaviour: Insights from the empirical literature," Energy Policy, Elsevier, vol. 144(C).
    5. Zhao, Bo & Xue, Meidong & Zhang, Xuesong & Wang, Caisheng & Zhao, Junhui, 2015. "An MAS based energy management system for a stand-alone microgrid at high altitude," Applied Energy, Elsevier, vol. 143(C), pages 251-261.
    6. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    7. Gulbinas, R. & Jain, R.K. & Taylor, J.E., 2014. "BizWatts: A modular socio-technical energy management system for empowering commercial building occupants to conserve energy," Applied Energy, Elsevier, vol. 136(C), pages 1076-1084.
    8. 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.
    9. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    10. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    11. Anderson, Kyle & Lee, SangHyun, 2016. "An empirically grounded model for simulating normative energy use feedback interventions," Applied Energy, Elsevier, vol. 173(C), pages 272-282.

    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. Ajla Cosic & Hana Cosic & Sebastian Ille, 2018. "Can nudges affect students' green behaviour? A field experiment," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 2(1), pages 107-111, March.
    2. Bartels, Lara & Kesternich, Martin, 2022. "Motivate the crowd or crowd- them out? The impact of local government spending on the voluntary provision of a green public good," ZEW Discussion Papers 22-040, ZEW - Leibniz Centre for European Economic Research.
    3. Denis Hilton & Nicolas Treich & Gaetan Lazzara & Philippe Tendil, 2018. "Designing effective nudges that satisfy ethical constraints: the case of environmentally responsible behaviour," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 17(1), pages 27-38, November.
    4. Bonan, Jacopo & Battiston, Pietro & Bleck, Jaimie & LeMay-Boucher, Philippe & Pareglio, Stefano & Sarr, Bassirou & Tavoni, Massimo, 2021. "Social interaction and technology adoption: Experimental evidence from improved cookstoves in Mali," World Development, Elsevier, vol. 144(C).
    5. Castro-Santa, Juana & Drews, Stefan & Bergh, Jeroen van den, 2023. "Nudging low-carbon consumption through advertising and social norms," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 102(C).
    6. Vandenbroele, Jolien & Slabbinck, Hendrik & Van Kerckhove, Anneleen & Vermeir, Iris, 2021. "Mock meat in the butchery: Nudging consumers toward meat substitutes," Organizational Behavior and Human Decision Processes, Elsevier, vol. 163(C), pages 105-116.
    7. María Laura Alzúa & Habiba Djebbari & Amy J. Pickering, 2020. "A Community-Based Program Promotes Sanitation," Economic Development and Cultural Change, University of Chicago Press, vol. 68(2), pages 357-390.
    8. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
    9. Choi, Sunghee & Hwang, Seok-Joon & Denzau, Arthur T., 2021. "Do households conserve electricity when they receive signals of greater consumption than neighbours? The Korean case," Energy, Elsevier, vol. 225(C).
    10. Löfgren, Åsa & Nordblom, Katarina, 2020. "A theoretical framework of decision making explaining the mechanisms of nudging," Journal of Economic Behavior & Organization, Elsevier, vol. 174(C), pages 1-12.
    11. Jason Delaney & Sarah Jacobson, 2016. "Payments or Persuasion: Common Pool Resource Management with Price and Non-price Measures," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(4), pages 747-772, December.
    12. Olsthoorn, Mark & Schleich, Joachim & Faure, Corinne, 2019. "Exploring the diffusion of low-energy houses: An empirical study in the European Union," Energy Policy, Elsevier, vol. 129(C), pages 1382-1393.
    13. Schmidt, Robert J., 2019. "Do injunctive or descriptive social norms elicited using coordination games better explain social preferences?," Working Papers 0668, University of Heidelberg, Department of Economics.
    14. Komatsu, Hidenori & Nishio, Ken-ichiro, 2015. "An experimental study on motivational change for electricity conservation by normative messages," Applied Energy, Elsevier, vol. 158(C), pages 35-43.
    15. Khosrowpour, Ardalan & Xie, Yimeng & Taylor, John E. & Hong, Yili, 2016. "One size does not fit all: Establishing the need for targeted eco-feedback," Applied Energy, Elsevier, vol. 184(C), pages 523-530.
    16. Holladay, J. Scott & Price, Michael K. & Wanamaker, Marianne, 2015. "The perverse impact of calling for energy conservation," Journal of Economic Behavior & Organization, Elsevier, vol. 110(C), pages 1-18.
    17. Sara Rafael Almeida & Joana Sousa Lourenco & Francois J. Dessart & Emanuele Ciriolo, 2017. "Insights from behavioural sciences to prevent and combat violence against women. Literature review," JRC Research Reports JRC103975, Joint Research Centre.
    18. Brade, Raphael, 2022. "Social Information and Educational Investment - Nudging Remedial Math Course Participation," MPRA Paper 113076, University Library of Munich, Germany.
    19. Hynes, Niki & Wilson, Juliette, 2016. "I do it, but don't tell anyone! Personal values, personal and social norms: Can social media play a role in changing pro-environmental behaviours?," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 349-359.
    20. Pinar Yildirim & Yanhao Wei & Christophe Bulte & Joy Lu, 2020. "Social network design for inducing effort," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 381-417, December.

    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:appene:v:105:y:2013:i:c:p:358-368. 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/405891/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.