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As part of an effort to be a world leader in international efforts in reducing atmospheric carbon dioxide levels, the UK Government has set itself ambitious targets to reduce carbon dioxide emissions by 80% relative to 1990 levels by 2050. To meet this target, there is a strong emphasis in reducing carbon emissions from the domestic sector through the reduction of energy consumption in UK households by improving the energy efficiency of the housing stock, and the behaviours of the occupants. The Department of Energy and Climate Change have indicated that Local Authorities in England are potentially to work in partnership with businesses and community organizations to facilitate delivery; and as a promoter of domestic energy efficiency policies. Consultation with 11 Local Authorities across England confirmed that they are lacking a reliable mechanism that can detect areas within their administrative boundaries that are most in need of intervention to improve the energy efficiency of the housing stock. For the year 2008 the regression models demonstrate that geographical variations in the size of the house, median household income, and air temperature account for 64% of the variation in English domestic gas consumption, and that variations in the size of the house, median household income, and proportion of households connected to the national gas grid account for 73% of the variation in domestic electricity consumption. The predicted values from these regression models serve as benchmarks of domestic gas and electricity consumption in England having accounted for household income, house size, house type, tenure, and climatic differences and could be used to identify areas within Local Authorities with higher than expected energy consumption for energy efficiency interventions. These results contribute to the wider academic debate over how best to achieve the overall aims of household CO2 reductions by moving beyond a purely technical or behavioural-based approach to reducing domestic energy consumption.

This thesis is concerned with measuring the fabric thermal performance of houses. This is important because the evidence shows that predictions of performance, based upon a summation of expected elemental performance, are prone to significant inaccuracy and in-situ performance is invariably worse than expected the so-called performance gap . Accurate knowledge of the thermal performance of houses could cause a shift in the way that houses are built, retrofitted and managed. It would enable quality-assurance of newly-built and retrofitted houses, driving an improvement in the energy performance of the housing stock. The current barrier to achieving these benefits is that existing measurement methods are impractically invasive for use on a mass-scale. The aim of this research is to address this issue by developing non-invasive fabric thermal performance measurement methods for houses. The co-heating test is currently the most used method for measuring whole-house fabric thermal performance; it is used to measure the Heat Loss Coefficient (HLC) of a house, which is a measure of the rate of heat loss with units of Watts per degree Kelvin. It has been used extensively in a research context, but its more widespread use has been limited. This is due to a lack of confidence in the accuracy of its results and the test s invasiveness (the house must be vacant for two weeks during testing, which has so far been limited to the winter months, and testing cannot be carried out in newly-built houses for a period of approximately one year due to the drying out period). To build confidence in the results of co-heating testing, the precision with which test results can be reported was determined by the combination of a sensitivity analysis to quantify measurement errors, and an analysis of the reproducibility of the test. Reproducibility refers to the precision of a measurement when test results are obtained in different locations, with different operators and equipment. The analysis of the reproducibility of the test was based upon a direct comparison of seven co-heating tests carried out by different teams in a single building. This is the first such analysis and therefore provides a uniquely powerful analysis of the co-heating test. The reproducibility and sensitivity analyses showed that, provided best practise data collection and analysis methods are followed, the HLC measured by a co-heating test can be reported with an uncertainty of ± 10%. The sensitivity analysis identified solar heat gains as the largest source of measurement error in co-heating tests. In response, a new approach for co-heating data collection and analysis, called the facade solar gain estimation method, has been developed and successfully demonstrated. This method offers a clear advancement upon existing analysis methods, which were shown to be prone to inaccuracy due to inappropriate statistical assumptions. The facade method allowed co-heating tests to be carried out with accuracy during the summer months, which has not previously been considered feasible. The demonstration of the facade method included a direct comparison against other reported methods for estimating solar gains. The comparison was carried out for co-heating tests undertaken in three buildings, with testing taking place in different seasons (winter, summer, and spring or autumn) in each case. This comparison provides a unique analysis of the ability of the different solar gain estimation methods to return accurate measurements of a house s HLC in a wide variety of weather conditions. Building on these results, a testing method was developed: the Loughborough In-Use Heat Balance (LIUHB). The LIUHB is a non-invasive measurement method, designed and tested in this study, which can measure the HLC of a house with an accuracy of ± 15% while it is occupied and used as normal. Measurements of energy consumption and internal temperature are discreetly collected over a period of three weeks, and combined with data collected at a local weather station to inform an energy balance, from which the HLC is calculated. This low impact monitoring approach removes the barriers to fabric thermal performance testing on a mass scale. The LIUHB has been successfully demonstrated in several comparative trials versus a baseline measurement provided by the co-heating test. The trials have included the application of extreme examples of synthetic occupancy conditions, testing in an occupied house, and quantification of the effects of a retrofit. Subject to further validation, the LIUHB has the potential to deliver many of the benefits associated with mass-scale measurement and quality assurance of housing performance.