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

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  • 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.

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  • 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
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    References listed on IDEAS

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    Cited by:

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    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.

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