Explaining daily energy demand in British housing using linked smart meter and socio-technical data in a bottom-up statistical model

Eoghan McKenna, Jessica Few, Ellen Webborn, Ben Anderson, Simon Elam, David Shipworth, Adam Cooper, Martin Pullinger, Tadj Oreszczyn

Research output: Contribution to journalArticlepeer-review

Abstract

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p-value 
Original languageEnglish
Article number111845
Number of pages21
JournalEnergy and buildings
Volume258
Early online date11 Jan 2022
DOIs
Publication statusPublished - 1 Mar 2022

Keywords / Materials (for Non-textual outputs)

  • Building
  • Energy
  • Heating
  • Gas
  • Electricity
  • Demand
  • Consumption
  • Household
  • Residential
  • Domestic
  • Smart meter
  • Daily
  • Longitudinal
  • Regression
  • Mixed effects
  • Random effects
  • Survey
  • Energy performance certificate
  • Weather
  • Temperature
  • Solar radiation
  • Building physics
  • Sociodemographic
  • Occupant
  • Behaviour
  • Attitudes

Fingerprint

Dive into the research topics of 'Explaining daily energy demand in British housing using linked smart meter and socio-technical data in a bottom-up statistical model'. Together they form a unique fingerprint.

Cite this