Household energy consumption: a study of the role of occupants

Steemers, Koen; Yun, Geun Young

doi:10.1080/09613210903186661

Cited by 249 publications

(141 citation statements)

References 12 publications

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“…Like most other sociodemographic determinants of energy consumption, the effects of income and age on energy consumption are intertwined with other factors such as housing characteristics [19,30,26,1,9,11]. The elderly show different attitudes in the use of appliances and housing choices (e.g., they may live in old owned homes) than the younger households, and therefore their energy consumption is different [43].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Age-Energy Consumption Profile in Residential Buildings

Estiri¹,

Zagheni²

2018

Preprint

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Age is an important proxy for many life course trajectories. The relationship between energy consumption and age is complex and understudied. We evaluated the existence and determinants of an age-energy consumption profile in the U.S. residential sector, using microdata from four waves of the Residential Energy Consumption Survey (RECS) in 1987, 1990, 2005, and 2009. We constructed pseudo cohorts from Bayesian generalized linear model estimates to draw micro-profiles for energy consumption across the life course. Overall, we found that residential energy consumption increases over the life course. Much of the increase in energy consumption is due to housing size. Variations in the the age-energy consumption micro-profiles can be described by concave and convex functions. In contrast to previous research that suggested that population aging would reduce energy demand, our results indicate that changing population age structure could amplify residential energy demand.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Age-Energy Consumption Profile in Residential Buildings

Estiri¹,

Zagheni²

2018

Preprint

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“…However, by 1993, that figure rose to more than 98% (Shimberg Center for Housing Studies, 1994). Against that trend, recent analysis of the U.S. Department of Energy's (DOE) Residential Energy Consumption Survey data has shown that central cooling and heat pump systems (as opposed to zoned window or wall systems) are correlated with considerably higher space conditioning energy use in spite of higher machine efficiencies (Steemers & Yun, 2009). Almost 95% of the supply cooling ducts are located in the attic space, leading to large cooling leakage and conduction losses for central systems.…”

Section: Introductionmentioning

confidence: 99%

Achieving Very High Efficiency and Net Zero Energy in an Existing Home in a Hot-Humid Climate. Long-Term Utility and Monitoring Data

Parker

Sherwin

2012

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“…These behaviours were surveyed in a measurement of hours on a weekly basis, such as the total hours the occupant spent in the bedroom in a typical week. Household characteristics included socio-demographic variables shown to influence energy consumption in previous studies (Steemers and Yun 2009;GuerraSantin and Itard 2010;Chen et al 2013): age, tenure type, household size, household type/composition, work-status, occupation, education and household income.…”

Section: Data Collectionmentioning

confidence: 99%

“…This is one of the most important areas being targeted by the Government for reducing energy usage and increasing climate security (DTI 2007). Occupant behaviour has been demonstrated to have a significant impact on household energy consumption (Steemers and Yun 2009;Gram-Hanssen 2004). Meanwhile, the predicted energy savings associated with energyefficient technologies frequently exceed actual savings made due to behavioural factors (Stern 1985;GramHanssen et al 2012;Sunikka-Blank and Galvin 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the predicted energy savings associated with energyefficient technologies frequently exceed actual savings made due to behavioural factors (Stern 1985;GramHanssen et al 2012;Sunikka-Blank and Galvin 2012). These behavioural factors may be categorised into socio-economic variables (Steemers and Yun 2009;Belaïd 2016), lifestyle groups (Guerra-Santin and Itard 2010) and socio-material configurations (GramHanssen 2010), in which social and material worlds are considered as inextricably entwined (Beaulieu et al 2016). They may also be explained by a rebound effect relating to higher comfort expectations (Sorrell et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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Household archetypes and behavioural patterns in UK domestic energy use

Ben

Steemers

2018

Energy Efficiency

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Variations in household behaviour often lead to a mismatch between actual and estimated energy performance at home. More detailed information on behavioural variables could help in improving the prediction of energy consumption and enabling policy interventions responding to different household groups. This research aims to identify household archetypes and behavioural patterns in order to allow a targeted approach in energy-saving policy and retrofit improvement. It employed a statistical approach to cluster households based on empirical data collected from a household survey in Cambridge, UK. Factor analysis was used to identify behavioural factors. Based on the commonalities of variables under each factor, five factors were defined: (1) main space heating, (2) auxiliary space use, (3) main space use, (4) auxiliary space heating and (5) use of appliances. Statistical pattern analysis was then applied to develop behavioural patterns. These patterns were derived based on their factor scores. Finally, non-parametric correlation analysis was carried out in order to determine the relationship between behavioural factors and the following: household or dwelling characteristics, comfort and energy use for creating household archetypes. After significant correlations were found between behavioural factors and other variables, five archetypes were identified: (1) active spenders, (2) conscious occupiers, (3) average users, (4) conservers and (5) inactive users. Among these archetypes, households with a larger house, higher energy use and more complex household composition tended to have longer hours of main space heating, while larger and more complex households tended to use the main space of their dwellings for longer. Using these archetypes allows for a better integration of occupant behaviour into the technically oriented efficiency paradigm. This tailored approach provides a gateway to developing more effective policies and low energy strategies geared towards specific households.

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Household energy consumption: a study of the role of occupants

Cited by 249 publications

References 12 publications

Evaluating the Age-Energy Consumption Profile in Residential Buildings

Evaluating the Age-Energy Consumption Profile in Residential Buildings

Achieving Very High Efficiency and Net Zero Energy in an Existing Home in a Hot-Humid Climate. Long-Term Utility and Monitoring Data

Household archetypes and behavioural patterns in UK domestic energy use

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