Determinants of winter indoor temperatures in low income households in England

Oreszczyn, Tadj; Hong, Sung Yong; Ridley, Ian; Wilkinson, Paul

doi:10.1016/j.enbuild.2005.06.006

Cited by 179 publications

(141 citation statements)

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“…Some of the significant predictors were the presence of children or retired occupants, building characteristics such as roof insulation, wall U-values, building type and the presence of thermostats or thermostatic radiator valves. Oreszczyn, Hong, Ridley, and Wilkinson (2006) found that similar socio-demographic variables, such as the age of householders and number of people, and buildingdemographic variables, such as construction and thermal efficiency, influenced internal temperatures of low-income households. Similarly, Hunt and Gidman (1982) found that internal temperatures in their UK sample varied according to dwelling type and tenure.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research, as discussed in the introduction, has shown that a number of socio-demographic and building-demographic variables are related to energy consumption and temperatures (Cayla et al, 2010;Hunt & Gidman, 1982;Kane, Firth, Allinson, Irvine, & Lomas, 2010;Kelly et al, 2013;Oreszczyn et al, 2006). Hence, the need to relate variables of both types to cluster membership.…”

Section: Relationship Of Clusters To Building-and Socio-demographic Vmentioning

confidence: 99%

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The shape of warmth: temperature profiles in living rooms

Huebner

McMichael

Shipworth

et al. 2014

Building Research & Information

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The most commonly used family of models in representing the UK building stock, BREDEM (the Building Research Establishment Domestic Energy Models), assume that all homes exhibit the same heating pattern and hence can be expected to have similar temperature profiles over the course of a day. The presented research shows that homes differ significantly in their respective temperature profile over the course of the day. A cluster analysis performed on temperature data from 275 living rooms in English homes over three winter months resulted in four different clusters of temperature profiles. The clusters differ significantly in their shape, as revealed by visual inspection, and supported by significant differences in minimum and maximum temperatures and temperature variability across the day. About 40% of homes showed a bimodal temperature pattern as assumed under BREDEM. However, the remaining 60% showed very different profiles. These findings challenge the assumption that one standard pattern fits all homes. Different temperature demand profiles have important implications for future peak power demands, particularly if domestic space heating is switched to electricity. It is also helpful for relating occupant demographics to appropriate forms of fabric retrofit.

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Section: Introductionmentioning

confidence: 99%

Section: Relationship Of Clusters To Building-and Socio-demographic Vmentioning

confidence: 99%

The shape of warmth: temperature profiles in living rooms

Huebner

McMichael

Shipworth

et al. 2014

Building Research & Information

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“…5 This is not surprising as in the UK wintertime space heating energy demands are a major source of greenhouse gas emissions and under heating of homes is a significant health risk. Such studies include, for example, 1600 low income households (Oreszczyn et al, 2006), 427 homes in the CaRB study (Shipworth et al, 2009), 14 lowenergy homes monitored in Milton Keynes (Summerfield et al, 2007) and 25 households in Northern Ireland (Yohanis et al, 2010). The most extensive field survey (Hunt & Gidman, 1982) measured spot temperatures in each room of 100 homes in February and March 1978. 111,000 homes (ONS, 2010), Leicester is the UK's 15 th largest city and has households that cover a wide range of socio-economic categories, from affluent to the most disadvantaged.…”

Section: Household Survey and Temperature Measurementsmentioning

confidence: 99%

Summertime temperatures and thermal comfort in UK homes

Lomas

Kane

2013

Building Research & Information

113

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[213 words]Internal summertime temperatures measured in 268 homes in the UK city of Leicester are reported. The hourly data was collected from living rooms and bedrooms during the summer of 2009, which was generally cool but with a short hot spell. Some household interviews were conducted. The sample of homes is statistically representative of the socio-technical characteristics of the city's housing stock. The data provides insight into the influence of house construction, energy system usage and occupant characteristics on the incidence of elevated temperatures and thermal discomfort.The warmest homes were amongst the 13% that were heated. Significantly more of these were occupied by those over 70 who are particularly vulnerable to high temperatures. The national heatwave plan might usefully caution against summertime heating.Temperatures in the 230 free-running homes were analysed using both static criteria and criteria associated with the BSEN15251 adaptive thermal comfort model. These indicated that that flats tended to be significantly warmer than other house types. Solid wall homes and detached houses tended to be significantly cooler.It is argued that adaptive criteria provide a valuable and credible framework for assessing internal temperatures in free-running UK homes. However, the temperatures in the Leicester homes were much lower than anticipated by the BSEN15251 model. Numerous possible reasons for this discrepancy are discussed.

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“…Whilst the specific method of using Data Loggers (DLs) with qualitative interviewing is under-used in fuel poverty research, it fits with more general methodological approaches such as those described as mixed methods, triangulation of methods and cross-validation [25][26][27] . The advantages of triangulation of methods has been applied in several studies and the method has developed to combine different methodologies and fields of research 28,29 .…”

Section: -18mentioning

confidence: 99%