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Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

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  • Morris, Peter
  • Vine, Desley
  • Buys, Laurie

Abstract

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

Suggested Citation

  • Morris, Peter & Vine, Desley & Buys, Laurie, 2015. "Application of a Bayesian Network complex system model to a successful community electricity demand reduction program," Energy, Elsevier, vol. 84(C), pages 63-74.
    • Handle: RePEc:eee:energy:v:84:y:2015:i:c:p:63-74
    DOI: 10.1016/j.energy.2015.02.019
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    References listed on IDEAS

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    6. Tang, Lei & Wang, Xifan & Wang, Xiuli & Shao, Chengcheng & Liu, Shiyu & Tian, Shijun, 2019. "Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory," Energy, Elsevier, vol. 167(C), pages 1144-1154.
    7. Nikolaos Iliopoulos & Motoharu Onuki & Miguel Esteban, 2021. "Shedding Light on the Factors That Influence Residential Demand Response in Japan," Energies, MDPI, vol. 14(10), pages 1-23, May.
    8. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    9. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    10. Borunda, Mónica & Jaramillo, O.A. & Reyes, Alberto & Ibargüengoytia, Pablo H., 2016. "Bayesian networks in renewable energy systems: A bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 32-45.

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