Skip to main content

Advertisement

SpringerLink
Log in

Targeted selection of participants for energy efficiency programs using genetic agent-based (GAB) framework

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

Many studies show that the human energy-related behaviors have a significant impact on the return of Energy Efficiency Programs (EEPs). However, studies that aimed at increasing the energy savings from the EEPs are still limited. In this paper, a Genetic Agent-Based (GAB) framework has been proposed to enhance the return of a typical EEP by simulating social network and energy behavior attributes and finding the best participants among a target community. Several attributes are considered for creating the agent-based model of households and numerically representing their interactions with the EEP or within their social network. The improvement of the EEP using the GAB framework is tested on a social network consisting of 56 households. The simulation results show that by accurately selecting participants using the presented framework, the amount of energy saving could increase up to ten times. This ultimately indicates the considerable impact of the social network on the EEP performance. In other words, to have an efficient EEP in the long term, the social network attributes such as network degree and strength of connections should be also considered in decision-making along with the energy-related attributes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abdessalem, T., & Labidi, E. (2016). Economic analysis of the energy-efficient household appliances and the rebound effect. Energy Efficiency, 9, 605–620.

    Article  Google Scholar 

  • Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25, 273–291.

    Article  Google Scholar 

  • Anderson, K., Lee, S., & Menassa, C. (2013). Impact of social network type and structure on modeling normative energy use behavior interventions. Journal of Computing in Civil Engineering, 28, 30–39.

    Article  Google Scholar 

  • Anderson, K., Song, K., Lee, S., Krupka, E., Lee, H., & Park, M. (2017). Longitudinal analysis of normative energy use feedback on dormitory occupants. Applied Energy, 189, 623–639.

    Article  Google Scholar 

  • Azar, E., & Al Ansari, H. (2017). Multilayer agent-based modeling and social network framework to evaluate energy feedback methods for groups of buildings. Journal of Computing in Civil Engineering, 31, 04017007.

    Article  Google Scholar 

  • Azar, E., & Menassa, C. C. (2013). Framework to evaluate energy-saving potential from occupancy interventions in typical commercial buildings in the United States. Journal of Computing in Civil Engineering, 28, 63–78.

    Article  Google Scholar 

  • Bastani, M. S., Asadi, S., & Anumba, C. J. (2016). Application of bass diffusion theory to simulate the impact of feedback and word of mouth on occupants’ behavior in commercial buildings: An agent-based approach. Journal of Architectural Engineering, 22, 04016013.

    Article  Google Scholar 

  • Chen, J., Taylor, J. E., & Wei, H.-H. (2012). Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation. Energy and Buildings, 47, 515–524.

    Article  Google Scholar 

  • Cheung, C., Fuller, R., & Luther, M. (2005). Energy-efficient envelope design for high-rise apartments. Energy and Buildings, 37, 37–48.

    Article  Google Scholar 

  • Cho, Y., Koo, Y., Huh, S.-Y., & Lee, M. (2015). Evaluation of a consumer incentive program for an energy-efficient product in South Korea. Energy Efficiency, 8, 745–757.

    Article  Google Scholar 

  • Deffuant, G., Amblard, F., Weisbuch, G., & Faure, T. (2002). How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation, 5.

  • Delzendeh, E., Wu, S., Lee, A., & Zhou, Y. (2017). The impact of occupants’ behaviours on building energy analysis: A research review. Renewable and Sustainable Energy Reviews, 80, 1061–1071.

    Article  Google Scholar 

  • Diamond, R. C. (1984). Energy Use among the Low-income Elderly: A Closer Look. Lawrence Berkeley National Laboratory. LBNL Report #: LBL-17593. Retrieved from https://escholarship.org/uc/item/75m8q8pf.

  • Dougherty, A., & van de Grift, S. C. (2016). Behavioral energy feedback program evaluations: a survey of current knowledge and a call to action. Energy Efficiency, 9, 899–909.

    Article  Google Scholar 

  • Du, F., Zhang, J., Li, H., Yan, J., Galloway, S., & Lo, K. L. (2016). Modelling the impact of social network on energy savings. Applied Energy, 178, 56–65.

    Article  Google Scholar 

  • Ekpenyong, U. E., Zhang, J., & Xia, X. (2014). Mathematical modelling for the social impact to energy efficiency savings. Energy and Buildings, 84, 344–351.

    Article  Google Scholar 

  • Ekpenyong, U. E., Zhang, J., & Xia, X. (2015). How information propagation in social networks can improve energy savings based on time of use tariff. Sustainable Cities and Society, 19, 26–33.

    Article  Google Scholar 

  • Francisco, A., Truong, H., Khosrowpour, A., Taylor, J. E., & Mohammadi, N. (2018). Occupant perceptions of building information model-based energy visualizations in eco-feedback systems. Applied Energy, 221, 220–228.

    Article  Google Scholar 

  • Friendkin, N. E. (2001). Norm formation in social influence networks. Social Networks, 23, 167–189.

    Article  Google Scholar 

  • Gynther, L., Mikkonen, I., & Smits, A. (2012). Evaluation of European energy behavioural change programmes. Energy Efficiency, 5, 67–82.

    Article  Google Scholar 

  • Hagberg, A., Swart, P., & Schult, D. (2008). Exploring network structure, dynamics, and function using NetworkX. Los Alamos: Los Alamos National Lab.(LANL).

    Google Scholar 

  • Hanus, N., Wong-Parodi, G., Small, M. J., & Grossmann, I. (2018). The role of psychology and social influences in energy efficiency adoption. Energy Efficiency, 11, 371–391.

    Article  Google Scholar 

  • Harvey, L. D. (2009). Reducing energy use in the buildings sector: measures, costs, and examples. Energy Efficiency, 2, 139–163.

    Article  Google Scholar 

  • Hoicka, C. E., & Parker, P. (2018). Assessing the adoption of the house as a system approach to residential energy efficiency programs. Energy Efficiency, 11, 295–313.

    Article  Google Scholar 

  • Kinnear, K. E., Langdon, W. B., Spector, L., Angeline, P. J., & O'reilly, U.-M. (1999). Advances in genetic programming. MIT press.

  • Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87–112.

    Article  Google Scholar 

  • Lanzisera, S., Nordman, B., & Brown, R. E. (2012). Data network equipment energy use and savings potential in buildings. Energy Efficiency, 5, 149–162.

    Article  Google Scholar 

  • Ma, G., Lin, J., & Li, N. (2018). Longitudinal assessment of the behavior-changing effect of app-based eco-feedback in residential buildings. Energy and Buildings, 159, 486–494.

    Article  Google Scholar 

  • Macal, C. M. (2016). Everything you need to know about agent-based modelling and simulation. Journal of Simulation, 10, 144–156.

    Article  Google Scholar 

  • Massey Jr., F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American Statistical Association, 46, 68–78.

    Article  Google Scholar 

  • Morgenstern, P., Raslan, R., & Huebner, G. (2016). Applicability, potential and limitations of staff-centred energy conservation initiatives in English hospitals. Energy Efficiency, 9, 27–48.

    Article  Google Scholar 

  • O’connor, N., & Macur, R. (2018). Engaging residents in affordable housing—Resident engagement pilot at Denver housing authority Westridge apartments. Energy Efficiency, 1–16.

  • Parker, D. S., Hoak, D., & Cummings, J. (2008). Pilot evaluation of energy savings from residential energy demand feedback devices. Cocoa: Florida Solar Energy Center.

    Book  Google Scholar 

  • Patro, S., & Sahu, K. K. (2015). Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462.

  • Peschiera, G., & Taylor, J. E. (2012). The impact of peer network position on electricity consumption in building occupant networks utilizing energy feedback systems. Energy and Buildings, 49, 584–590.

    Article  Google Scholar 

  • Sauma, E., Vera, S., Osorio, K., & Valenzuela, D. (2016). Design of a methodology for impact assessment of energy efficiency programs: measuring indirect effects in the Chilean case. Energy Efficiency, 9, 699–721.

    Article  Google Scholar 

  • Shimokawa, M., & Tezuka, T. (2014). Development of the “home energy conservation support program” and its effects on family behavior. Applied Energy, 114, 654–662.

    Article  Google Scholar 

  • Sorrell, S., Dimitropoulos, J., & Sommerville, M. (2009). Empirical estimates of the direct rebound effect: a review. Energy Policy, 37, 1356–1371.

    Article  Google Scholar 

  • Vine, E., Sullivan, M., Lutzenhiser, L., Blumstein, C., & Miller, B. (2014). Experimentation and the evaluation of energy efficiency programs. Energy Efficiency, 7, 627–640.

    Article  Google Scholar 

  • Winther, T., & Wilhite, H. (2015). An analysis of the household energy rebound effect from a practice perspective: spatial and temporal dimensions. Energy Efficiency, 8, 595–607.

    Article  Google Scholar 

  • Zarei, M. & Maghrebi, M. 2020. Improving Efficiency of Normative Interventions by Characteristic-Based Selection of Households: An Agent-Based Approach. Journal of Computing in Civil Engineering, 34(1), 04019042.

  • Zhang, T., Siebers, P.-O., & Aickelin, U. (2011). Modelling electricity consumption in office buildings: an agent based approach. Energy and Buildings, 43, 2882–2892.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Mohammad Zarei & Mojtaba Maghrebi

Authors
  1. Mohammad Zarei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mojtaba Maghrebi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mojtaba Maghrebi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarei, M., Maghrebi, M. Targeted selection of participants for energy efficiency programs using genetic agent-based (GAB) framework. Energy Efficiency 13, 823–833 (2020). https://doi.org/10.1007/s12053-020-09841-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-020-09841-z

Keywords

Search

Navigation