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

• Data from in-depth semi-structured interviews is used with a complex systems BN model.
• The BN model accepts data across social, economic and technical systems.
• The BN model illustrated the number of factors that influence peak demand reduction.
• The BN model was able to predict and explain the project's achieved success.

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.