Author(s)

Desley Vine^1*, Laurie Buys², Jim Lewis³, Peter Morris²

¹School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia.
²School of Design, Creative Industries Faculty, Queensland University of Technology, Brisbane, Australia.
³Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia.

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

Peak Electricity Demand, Residential Electricity, Complex Systems Modelling, Customer-Industry-Engagement

Vine, D. , Buys, L. , Lewis, J. and Morris, P. (2016) Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction. Open Journal of Energy Efficiency, 5, 31-47. doi: 10.4236/ojee.2016.52004.