Time-series analysis of the effects of building and household features on residential end-use energy

Soo Jin Lee, Seung Yeong Song

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This study aimed to comprehensively consider dwelling and household features, analyze their influence on the energy consumption and derive valid determinants for each end-use: heating, cooling, DHW, lighting, electric appliances, and cooking. To this end, information about dwelling and household features was collected through a field survey of 154 households in apartment complexes, representative of residential buildings in South Korea. In addition, a system was installed to measure energy consumption separately for each end-use. The end-use energy consumption data were collected during two periods: 2017 (May 2017–Apr. 2018) and 2019 (Jan.–Dec. 2019). Four models were created to analyze the influence of features on end-use energy consumption for each period. Model 1 was designed to analyze variables with the greatest influence, and Models 2–4 were designed to analyze the influence of physical building features, sociodemographic features, and household appliance-use characteristics. Multiple regression analysis was performed to derive the variables with the greatest influence and the valid variables for each characteristic category, and the influence magnitude was analyzed. These variables with the greatest influence were the same for 2017 and 2019, except for DHW. These variables were the area for exclusive use for the energy consumption of heating and electric appliances; the number of household members for lighting and cooking; the air conditioner operating hours for cooling. Regarding influence by the characteristic category, heating energy consumption was dominantly affected by physical building factor, cooling and electric appliances were mainly affected by household appliance-use characteristics, and DHW and cooking were greatly affected by sociodemographics. As for lighting it was evenly influenced by physical building factors, sociodemographics, and household appliance-use characteristics.

Original languageEnglish
Article number118722
JournalApplied Energy
StatePublished - 15 Apr 2022

Bibliographical note

Funding Information:
This research was supported by a grant (22SHTD-B157018-03) from AI Integrated Smart Housing Technology Development (SHTD) funded by the Ministry of Land, Infrastructure and Transport of the Korean government.

Publisher Copyright:
© 2022 Elsevier Ltd


  • Determinants
  • Dwelling and household features
  • Multiple regression analysis
  • Residential end-use energy
  • Time-series analysis


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