Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions

Gregory Sewell, Stephanie Gauthier, Patrick James and Sebastian Stein ()
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Gregory Sewell: Energy and Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering and Physical Sciences, University of Southampton, Boldrewood Campus, Southampton SO16 7QF, UK
Stephanie Gauthier: Energy and Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering and Physical Sciences, University of Southampton, Boldrewood Campus, Southampton SO16 7QF, UK
Patrick James: Energy and Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering and Physical Sciences, University of Southampton, Boldrewood Campus, Southampton SO16 7QF, UK
Sebastian Stein: School of Electronics and Computer Science, University of Southampton, Highfield Campus, University Road, Southampton SO17 1BJ, UK

Energies, 2024, vol. 17, issue 18, 1-21

Abstract: This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes ( n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap.

Keywords: domestic energy; occupant behaviour; lasso regression; energy performance gap; SAP (standardised assessment procedure) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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