Share This Article:

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

Full-Text HTML XML Download Download as PDF (Size:2090KB) PP. 31-47
DOI: 10.4236/ojee.2016.52004    1,041 Downloads   1,204 Views  


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.

Cite this paper

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.


[1] Energy Rating. Managing Peak Demand.
[2] Chester, L. (2012) Explainer: What Is Peak Power and How Does It Affect Prices? The Conversation, 2012.
[3] Moloney, S., Horne, R.E. and Fien, J. (2010) Transitioning to Low Carbon Communities—From Behaviour Change to Systemic Change: Lessons from Australia. Energy Policy, 38, 7614-7623.
[4] Bartusch, C., et al. (2011) Introducing a Demand-Based Electricity Distribution Tariff in the Residential Sector: Demand Response and Customer Perception. Energy Policy, 39, 5008.
[5] Guy, S. (2006) Designing Urban Knowledge: Competing Perspectives on Energy and Buildings. Environment and Planning C: Government and Policy, 24, 645-659.
[6] Keirstead, J. (2006) Evaluating the Applicability of Integrated Domestic Energy Consumption Frameworks in the UK. Energy Policy, 34, 3065-3077.
[7] Stern, P.C. (2011) Contributions of Psychology to Limiting Climate Change. American Psychologist, 66, 303-314.
[8] Lutzenhiser, L. (1993) Social and Behavioral Aspects of Energy Use. Annual Review of Energy and the Environment, 18, 247-289.
[9] Kowsari, R. and Zerriffi, H. (2011) Three Dimensional Energy Profile: A Conceptual Framework for Assessing Household Energy Use. Energy Policy, 39, 7505-7517.
[10] Stern, P.C. (1999) Information, Incentives, and Proenvironmental Consumer Behavior. Journal of Consumer Policy, 22, 461-478.
[11] Hinchliffe, S. (1996) Helping the Earth Begins at Home. The Social Construction of Socio-Environmental Responsibilities. Global Environmental Change, 6, 53-62.
[12] Wilson, C.M. and Price, C.W. (2010) Do Consumers Switch to the Best Supplier? Oxford Economic Papers, gpq006.
[13] Laitner, J.A. (2007) The Contribution of the Social Sciences to the Energy Challenge. ACEEE, Washington.
[14] Wilhite, H., et al. (2000) The Legacy of Twenty Years of Energy Demand Management: We Know More about Individual Behaviour but Next to Nothing about Demand. In: Jochem, E., Sathaye, J. and Bouille, D., Eds., Society, Behaviour, and Climate Change Mitigation, Kluwer Academic Publishers, Dordrecht; New York, 109-126.
[15] Heiskanen, E. and Pantzar, M. (1997) Toward Sustainable Consumption: Two New Perspectives. Journal of Consumer Policy, 20, 409-442.
[16] Abrahamse, W., Steg, L., Vlek, C. and Rothengatter, T. (2005) A Review of Intervention Studies Aimed at Household Energy Conservation. Journal of Environmental Psychology, 25, 273-291.
[17] Benders, R., Kok, R., Moll, H.C., Wiersma, G. and Noorman, K.J. (2006) New Approaches for Household Energy Conservation—In Search of Personal Household Energy Budgets and Energy Reduction Options. Energy Policy, 34, 3612-3622.
[18] Shove, E. (2003) Comfort, Cleanliness, and Convenience: The Social Organization of Normality. Berg Publishers, Oxford.
[19] Hazas, M., Friday, A. and Scott, J. (2011) Look Back before Leaping Forward: Four Decades of Domestic Energy Inquiry. IEEE Pervasive Computing, 10, 13-19.
[20] Vine, D., Buys, L. and Morris, P. (2013) The Effectiveness of Energy Feedback for Conservation and Peak Demand: A Literature Review. Open Journal of Energy Efficiency, 2, 7-15.
[21] Wood, G. and Newborough, M. (2003) Dynamic Energy-Consumption Indicators for Domestic Appliances: Environment, Behaviour and Design. Energy and Buildings, 35, 821-841.
[22] Poortinga, W., Steg, L. and Vlek, C. (2004) Values, Environmental Concern, and Environmental Behavior: A Study into Household Energy Use. Environment and Behavior, 36, 70-93.
[23] Abrahamse, W., Steg, L., Vlek, C. and Rothengatter, T. (2007) The Effect of Tailored Information, Goal Setting, and Tailored Feedback on Household Energy Use, Energy-Related Behaviors, and Behavioral Antecedents. Journal of Environmental Psychology, 27, 265-276.
[24] Steg, L. (2008) Promoting Household Energy Conservation. Energy Policy, 36, 4449-4453.
[25] Ehrhardt-Martinez, K., Donnelly, K.A. and Laitner, S. (2010) Advanced Metering Initiatives and Residential Feedback Programs: A Meta-Review for Household Electricity-Saving Opportunities. American Council for an Energy-Efficient Economy, Washington DC.
[26] Ehrhardt-Martinez, K., Laitner, S. and Donnelly, K.A. (2011) Chapter 10—Beyond the Meter: Enabling Better Home Energy Management. In: Perry, S.F., Ed., Energy, Sustainability and the Environment, Butterworth-Heinemann, Boston, 273-303.
[27] Burgess, J. and Nye, M. (2008) Re-Materialising Energy Use through Transparent Monitoring Systems. Energy Policy, 36, 4454-4459.
[28] Parnell, R. and Larsen, O.P. (2005) Informing the Development of Domestic Energy Efficiency Initiatives an Everyday Householder-Centered Framework. Environment and Behavior, 37, 787-807.
[29] Morris, P., Buys, L. and Vine, D. (2014) Moving from Outsider to Insider: Peer Status and Partnerships between Electricity Utilities and Residential Consumers. PLoS ONE, 9, e101189.
[30] Gardner, G.T. and Stern, P.C. (2002) Environmental Problems and Human Behavior. 2nd Edition, Pearson Custom Publishing, Boston.
[31] Cotton, M. and Devine-Wright, P. (2010) Making Electricity Networks “Visible”: Industry Actor Representations of “Publics” and Public Engagement in Infrastructure Planning. Public Understanding of Science, 21, 17-35.
[32] Darby, S. (2010) Smart Metering: What Potential for Householder Engagement? Building Research & Information, 38, 442-457.
[33] Honebein, P.C., Cammarano, R.F. and Boice, C. (2011) Building a Social Roadmap for the Smart Grid. The Electricity Journal, 24, 78-85.
[34] Lo, C.-H. and Ansari, N. (2012) The Progressive Smart Grid System from Both Power and Communications Aspects. IEEE Communications Surveys & Tutorials, 14, 799-821.
[35] Wilson, C. and Dowlatabadi, H. (2007) Models of Decision Making and Residential Energy Use. Annual Review of Environment and Resources, 32, 169-203.
[36] Lovell, H. (2005) Supply and Demand for Low Energy Housing in the UK: Insights from a Science and Technology Studies Approach. Housing Studies, 20, 815-829.
[37] Ambroz, K. and Derencin, A. (2010) Using a System Dynamics Approach for Identifying and Removing Management Model Inadequacy. Kybernetes, 39, 1583-1614.
[38] Kurtz, C.F. and Snowden, D.J. (2003) The New Dynamics of Strategy: Sense-Making in a Complex and Complicated World. IBM Systems Journal, 42, 462-483.
[39] Hovmand, P.S., Andersen, D.F., Rouwette, E., Richardson, G.P., Rux, K. and Calhoun A. (2012) Group Model-Building “Scripts” as a Collaborative Planning Tool. Systems Research and Behavioral Science, 29, 179-193.
[40] Korb, K.B. and Nicholson, A.E. (2010) Bayesian Artificial Intelligence. CRC Press, Boca Raton.
[41] Jensen, F.V. and Nielsen, T.D. (2007) Bayesian Networks and Decision Graphs. 2nd Edition, Springer, New York.
[42] Van Raaij, W.F. and Verhallen, T.M.M. (1983) A Behavioral Model of Residential Energy Use. Journal of Economic Psychology, 3, 39-63.
[43] Buys, L., et al. (2015) A Framework for Understanding and Generating Integrated Solutions for Residential Peak Energy Demand. PloS ONE, 10, e0121195.
[44] Lewis, J., et al. (2015) Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand. PloS ONE, 10, e0134086.
[45] Allcott, H. and Mullainathan, S. (2010) Behavioral Science and Energy Policy. Science, 327, 1204-1205.
[46] Rogers, E.M. (2010) Diffusion of Innovations. 4th Edition, Simon and Schuster, New York.
[47] Morris, P., Vine, D. and Buys, L. (2015) Application of a Bayesian Network Complex System Model to a Successful Community Electricity Demand Reduction Program. Energy, 84, 63-74.
[48] Schultz, P.W., Nolan, J.M., Cialdini, R.B., Goldstein, N.J. and Griskevicius, V. (2007) The Constructive, Destructive, and Reconstructive Power of Social Norms. Psychological Science, 18, 429-434.
[49] Darby, S. (2006) The Effectiveness of Feedback on Energy Consumption. A Review for DEFRA of the Literature on Metering, Billing and Direct Displays.
[50] Jaffe, A.B. and Stavins, R.N. (1994) The Energy-Efficiency Gap: What Does It Mean? Energy Policy, 22, 804-810.
[51] Weber, L. (1997) Some Reflections on Barriers to the Efficient Use of Energy. Energy Policy, 25, 833-835.
[52] Press, M. and Arnould, E.J. (2009) Constraints on Sustainable Energy Consumption: Market System and Public Policy Challenges and Opportunities. Journal of Public Policy & Marketing, 28, 102-113.
[53] Brown, M.A. (2001) Market Failures and Barriers as a Basis for Clean Energy Policies. Energy Policy, 29, 1197-1207.

comments powered by Disqus

Copyright © 2017 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.