Application of a Bayesian network complex system model examining the importance of customer-industry engagement to peak electricity demand reduction

Vine, Desley, Buys, Laurie, Lewis, Jim, & Morris, Peter (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, pp. 31-47.

View at publisher (open access)

Abstract

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.

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

5 since deposited on 03 Aug 2016
5 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 97881
Item Type: Journal Article
Refereed: Yes
Keywords: Peak Electricity Demand, Residential Electricity, Complex Systems Modelling, Customer-Industry-Engagement
DOI: 10.4236/ojee.2016.52004
ISSN: 2169-2645
Divisions: Current > Schools > School of Design
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Creative Industries Faculty
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright © 2016 by authors and Scientific Research Publishing Inc.
Copyright Statement: This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Abstract
Deposited On: 03 Aug 2016 01:11
Last Modified: 03 Aug 2016 23:58

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page