A Comprehensive Study on the Effect of Households’ Evolution on Residential Energy Consumption Patterns

  • Moulay Larbi Chalal
  • Medjdoub Benachir
  • Michael White
  • Golnaz Shahtahmassebi
  • Raid Shrahily
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 6)

Abstract

The residential sector accounts for approximately 27 and 17% of the world energy consumption and its CO2 emission, respectively. Thus, developing measures to reduce carbon dioxide emissions in this sector, which is highly associated with the rapidly increasing proportion of world’s urban population, is crucial to ensuring the sustainable development of the urban environment. However, the majority of the existing expertise on energy sustainability revolves around improving the thermal quality of the building envelop with lesser focus on the social and behavioural aspects of energy consumption. Given the importance of factors pertaining to the latter aspects, which are found to be responsible for 4–30% of the variation in residential energy consumption, this paper aims to address and explore for the first time the impact of the UK residents’ life-cycle evolution on their energy usage. To attain this, an official database encompassing around 5000 households observed over the course of 10 years was analysed with the help of specific statistical tests and procedures (e.g. logistic regression). First, logistic regression was employed to determine the socio-economic factors influencing households’ evolution from one state to another; consequently, future evolutionary models covering a 10-year window, were predicted. This was followed by analysing the effect of the predicted evolutionary models on the households’ gas and electricity usage patterns using point-biserial correlation. Finally, the findings suggest that households’ evolution have a significant effect on their energy consumption patterns. However, the magnitude and the direction of this effect is weak and mostly positive, respectively.

Keywords

Urban energy planning Household transitions Smart cities Energy forecasting Household projection 

References

  1. 1.
    DECC (2015) Energy consumption in the UK (2015)Google Scholar
  2. 2.
    CIA (2015) The world factbook. https://www.cia.gov/library/publications/the-world-factbook/fields/2212.html. Accessed 24 May 2016
  3. 3.
    Brounen D, Kok N, Quigley JM (2012) Residential energy use and conservation: economics and demographics. Eur Econ Rev 56:931–945CrossRefGoogle Scholar
  4. 4.
    Gill ZM, Tierney MJ, Pegg IM, Allan N (2010) Low-energy dwellings: the contribution of behaviours to actual performance. Build Res Inf 38:491–508CrossRefGoogle Scholar
  5. 5.
    Mansouri I, Newborough M, Probert D (1996) Energy consumption in UK households: impact of domestic electrical appliances. Appl Energy 54:211–285CrossRefGoogle Scholar
  6. 6.
    Sonderegger RC (1978) Movers and stayers: the resident’s contribution to variation across houses in energy consumption for space heating. Energy Build 1:313–324CrossRefGoogle Scholar
  7. 7.
    Van Raaij WF, Verhallen TMM (1983) A behavioral model of residential energy use. J Econ Psychol 3:39–63CrossRefGoogle Scholar
  8. 8.
    DECC (2012) United Kingdom housing energy fact fileGoogle Scholar
  9. 9.
    Frederiks ER, Stenner K, Hobman EV (2015) The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review. Energies 8:573–609CrossRefGoogle Scholar
  10. 10.
    Steg L, Vlek C (2009) Encouraging pro-environmental behaviour: an integrative review and research agenda. J Environ Psychol 29:309–317CrossRefGoogle Scholar
  11. 11.
    Bartiaux F, Gram-Hanssen K (2005) Socio-political factors influencing household electricity consumption : a comparison between Denmark and Belgium. In: ECEE Summer Study, pp 1313–1325Google Scholar
  12. 12.
    Druckman A, Jackson T (2008) Household energy consumption in the UK: a highly geographically and socio-economically disaggregated model. Energy Policy 36:3177–3192CrossRefGoogle Scholar
  13. 13.
    Genjo K, Tanabe SI, Matsumoto SI et al (2005) Relationship between possession of electric appliances and electricity for lighting and others in Japanese households. Energy Build 37:259–272.  https://doi.org/10.1016/j.enbuild.2004.06.025
  14. 14.
    Santin OG, Itard L, Visscher H (2009) The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock. Energy Build 41:1223–1232. http://dx.doi.org/10.1016/j.enbuild.2009.07.002
  15. 15.
    Santamouris M, Kapsis K, Korres D et al (2007) On the relation between the energy and social characteristics of the residential sector. Energy Build 39:893–905.  https://doi.org/10.1016/j.enbuild.2006.11.001
  16. 16.
    Wiesmann D, Azevedo IL, Ferrão P, Fernández JE (2011) Residential electricity consumption in Portugal: findings from top-down and bottom-up models. Energy Policy 39:2772–2779.  https://doi.org/10.1016/j.enpol.2011.02.047
  17. 17.
    Zhou S, Teng F (2013) Estimation of urban residential electricity demand in China using household survey data. Energy Policy 61:394–402.  https://doi.org/10.1016/j.enpol.2013.06.092
  18. 18.
    Longhi S (2014) Residential energy use and the relevance of changes in household circumstances. ISER Working Paper SeriesGoogle Scholar
  19. 19.
    Du RY, Kamakura WA (2006) Household life cycles and lifestyles in the United States. J Mark Res 43:121–132.  https://doi.org/10.1509/jmkr.43.1.121CrossRefGoogle Scholar
  20. 20.
    ISER (2016) British household panel survey (BHPS)—institute for social and economic research (ISER). https://www.iser.essex.ac.uk/bhps. Accessed 16 Apr 2016
  21. 21.
    Greene WWH (2012) Econometric analysisGoogle Scholar
  22. 22.
    DECC (2015) RPI: fuel & light: electricity (Jan 1987 = 100)—office for national statistics. https://www.ons.gov.uk/economy/inflationandpriceindices/timeseries/dobx. Accessed 18 Apr 2016
  23. 23.
    Hoaglin DC, Iglewicz B, Tukey JW (1986) Performance of some resistant rules for outlier labeling. J Am Stat Assoc 81:991–999MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Allison PD (2006) Fixed effects regression methods in SASGoogle Scholar
  25. 25.
    Thomas R, Have T, Kunselman AR et al (1998) Mixed effects logistic regression models for longitudinal binary response data with informative drop-out. Biometrics 367–383Google Scholar
  26. 26.
    ONS (2015) Births by parents’ characteristics in England and Wales: 2014Google Scholar
  27. 27.
    NCT (2014) Barriers remain for parents returning to work. https://www.nct.org.uk/press-release/barriers-remain-parents-returning-work
  28. 28.
    Goodman A, Greaves E (2010) Cohabitation, marriage and child outcomesGoogle Scholar
  29. 29.
    Whiting S (2010) Socio-demographic comparison between those UK families with up to two children and those with three or moreGoogle Scholar
  30. 30.
    ONS (2013) Home ownership and renting in England and Wales—detailed characteristicsGoogle Scholar
  31. 31.
    Kornbrot D (2005) Point biserial correlationGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nottingham Trent UniversityNottinghamUK

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