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)


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.


Urban energy planning Household transitions Smart cities Energy forecasting Household projection 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nottingham Trent UniversityNottinghamUK

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