Summertime temperatures and thermal comfort in UK homes

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

Summertime temperatures in 282 UK homes: thermal comfort and overheating risk

Summertime temperatures in UK homes are a matter of increasing concern, particularly because of global warming and an increased incidence of heat waves. Refurbishment adds to uncertainty about the resilience of UK homes to climate change. This paper examines internal summertime temperatures in the living and bedrooms of 282 homes in the UK city of Leicester. This is a statistically representative sample of the citys housing stock. The generally cool monitoring period included a short period of hot weather. Occupant behaviour had a significant impact on internal temperature, 13% of the homes were actively heated even during the spell of hot weather. In the 230 unheated homes, 28% of the living rooms and 88% of bedrooms were classed as severely overheated, as judged by the static, CIBSE, criteria. In contrast, 64% of the living rooms and 71% of the bedrooms were judged uncomfortably cool as defined by the BSEN15251 Cat II adaptive thermal comfort standard

Measured internal temperatures in UK homes: a time series analysis and modelling approach

This paper presents an analysis of internal air temperatures measured hourly in the living rooms of 230 domestic buildings in the city of Leicester, UK. Time series analysis is used to identify the mechanisms that shape room temperatures, during the summertime period of July and August, in rooms that are neither mechanically heated nor cooled, and to develop empirical models of room temperatures for use in predicting future temperatures based on past measured values and on future weather conditions. Such models can enable overheating risk alerts for homeowners and public authorities to be more accurately estimated and targeted

Measured internal temperatures in UK homes: a time series analysis and modelling approach

This paper presents an analysis of internal air temperatures measured hourly in the living rooms of 10 domestic buildings in the city of Leicester, UK. Time series analysis is used to develop empirical models of room temperatures in rooms that are neither mechanically heated nor cooled, during the summertime period of July and August 2009. The models are used in predicting future temperatures based on past measured values. Such models can enable overheating risk alerts for homeowners and public authorities to be more accurately estimated and targeted

Data-driven simple thermal models: the importance of the parameter estimates

A simple 1st order data-driven lumped parameter model of a domestic building is developed to explore the effect of using different model parameter values in the model outputs. The adequacy of the Ordinary Least Square estimation technique is explored. Results show that an improved fit to the measured data can be achieved by varying the initial model parameter values of capacitance (up to 78%), resistance (-46%) and effective window area (-59%). This highlights the importance of having a reference set of parameters based on the known physical characteristics of the building. Finally, the model residuals are deemed appropriate to inform the decision making process for further model development

Developing suitable thermal models for domestic buildings with Smart Home equipment

Smart Home controls are part of a Smart Home system and allow remote and automated control of heating systems. The key research question is: with the rapid advancement of new wireless and networked control products, which thermal modelling techniques are able to best make use of the real-time performance data arising from in-home sensors and predict the impact of using advanced controls to reduce energy demand and maximise comfort? As part of identifying suitable modelling approaches for Smart Homes, a lumped parameter model which builds on the work done by Bacher and Madsen (2011) using a data-driven “Grey box” model has been developed. The potential for using the measured data and the impacts of advanced controls for this modelling technique are discussed

Understanding occupant heating practices in UK dwellings

The 2008 Climate Change Act has committed the UK government to reduce CO2 emissions by 80% of 1990 levels by 2050. To meet this target a significant reduction in energy consumption will be required from domestic dwellings and in particular space heating which accounts for more than 50% of the energy used in the UK housing stock. The UK government has initiated a number of policies to reduce energy use from UK dwellings. Energy savings that result from energy efficiency improvements to dwellings have sometime been lower than expected as a result for the rebound effect. Discussion of the rebound effect has questioned whether these polices will result in the CO2 reductions required to meet the national targets. Large-scale survey research has shown that energy use is related to climate, built form of properties, efficiency of heating systems, socioeconomic indicators and occupant behaviour. Temperature monitoring studies have been undertaken to gain insight into how occupants heat their homes. If the variation in indoor temperatures can be explained by; (1) social determinants such as age, income and the number of household occupants and; (2) technical determinants such as house type, house age and level of insulation then this would enable energy efficiency initiatives (e.g. cavity wall installation or education programmes) to be targeted where they will be most effective. This paper presents preliminary results from a large-scale city-wide survey of over 500 homes in the city of Leicester, UK. Temperature measurements were recorded at hourly intervals over a nine month period for the living room and main bedroom spaces in over 300 homes. Household data, including socio-demographic information, was collected for each household. This dataset is used to investigate indoor temperatures across house types. The results confirm that house type is related to differences in indoor temperatures. Flats have the highest average temperatures while detached homes have the lowest. To gain insight into heated periods households with average evening temperatures were identified. It was found 45% of mid terrace properties had evening temperatures below 18°C and more than a third of detached and semi detached home also had cold evening temperatures. There are a number of reasons for low indoor temperatures in dwellings during occupied periods including inefficiency of buildings and heating systems, the inability of occupants to afford heating and personal choice. It is concluded that to meet Government CO2 reduction targets the rebound effect should be taken into account when calculating the energy savings expected from energy efficiency programmes. Further analysis is ongoing to identify how other social and technical factors relate to indoor temperatures. Multiple regression analysis will be used to identify how internal temperatures are correlated against a number of determinants including building characteristics (built form type, age, heating system type, heating controls) and household characteristics (age of occupants, income)

Variation of indoor temperatures and heating practices in UK dwellings

The UK government is committed to making significant reductions in CO2 emissions by 2050. To meet this target, a considerable reduction in energy consumption will be required from domestic dwellings and, in particular, space heating, which accounts for more than 50% of the energy used in the UK housing stock. Temperature monitoring studies in the UK have been undertaken, these can inform modellers of the variation in indoor temperatures throughout the housing stock. This information will allow energy modellers to better predict the indoor temperatures demanded by household occupants, and consequently improve the accuracy of energy predictions. This paper presents preliminary results from a large-scale city-wide survey of over 500 homes in Leicester, UK. Temperature measurements were recorded at hourly intervals in over 300 homes. Household data, including socio-demographic information, were collected for each household. This dataset is used to investigate the relationship between indoor temperatures and house type. The results confirm that house type is related to differences in indoor temperatures, but this relationship is not significant during heated periods. Further analysis is ongoing to identify how other social and technical factors relate to indoor temperatures

Does the age of the residents influence occupant heating practice in UK domestic buildings?

The UK Government is committed to reducing CO2 emissions by 80% of 1990 levels by 2050. To meet this target significant reductions in energy consumption are required from the UK housing stock. Space heating is the most significant end use of energy in residential buildings. Behaviour that relates to the direct interaction of residents with heating systems is termed occupant heating practice (OHP). More empirical evidence is required to determine if OHPs relate to socio-demographic and economic status of households. Improved knowledge of OHP will aid policy makers in successfully targeting energy efficiency measures. To build the evidence base for OHP, a large-scale city-wide housing survey was carried out in Leicester, UK in 2009-2010. Internal temperature measurements and details about household composition were collected in over 300 dwellings. These data are used to explore the links between OHP and the age of occupants. Results of the initial analysis suggest that older occupants demand higher living room temperatures but may heat a lower proportion of their dwelling. 36% of dwellings were observed to have lower than average temperatures. Continued analysis is required to find out if energy efficiency measures could improve the thermal comfort of occupants or if low temperatures are a result of short daily heating periods. A more detailed monitoring study is required to investigate the variation in internal temperatures throughout dwellings and to gain further insight into OHP

Decision support systems for domestic retrofit provision using smart home data streams

The scope of this paper is a study of the potential of decision support systems for retrofit provision in domestic buildings, using monitoring technologies and performance-based analysis. The key research question is: in the age of proliferation of cheap, mobile and networked sensing equipment, how can measured energy and performance data from multiple in-home sensors be utilised to accelerate building retrofit measures and energy demand reduction? Over the coming decade there will be a significant increase in the amount of measured data available from households, from national Smart Meter rollouts to personal Smart Home systems, which will provide unparalleled insights into how our homes are performing and how households are behaving. The new data streams from Smart Homes will challenge the prevailing research and policy initiatives for understanding and promoting energy-saving building retrofits. This work is part of a £1.5m UK Research Council funded project ‘REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’ (www.refitsmarthomes.org). Three methods are combined to give multiple perspectives of the research challenge: 1) A literature review on Smart Homes with a focus on academic progress to date in this area; 2) Results from actual Smart Home monitored data streams, as measured in an on-going study of UK-based Smart Homes; and 3) a discussion of performance-based analysis leading to insights in decision support system provision for Smart Building owners. The approach outlined in this work will be of significant interest to national governments when promoting Smart Meter roll-outs, to energy companies in promoting new services using Smart Home data and to the academic community in providing a foundation for future studies to meet the domestic building retrofit challenge