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The UK housing stock is responsible for some 27% of national energy demand and associated carbon dioxide emissions. 80% of this energy demand is due to heating (60%) and domestic hot water (20%), the former reflecting the poor average thermal integrity of the envelope of the homes comprising this stock. To support the formulation of policies and strategies to decarbonise the UK housing stock, a large number of increasingly sophisticated Housing Stock Energy Models (HSEMs) have been developed throughout the past 25 years. After describing the sources of data and the spatio-temporal granularity with which these data are available to represent this stock, as well as the physical and social phenomena that are modelled and the range of strategies employed to do so, this paper evaluates the 29 HSEMs that have been developed and deployed in the UK. In this we consider the models’ predictive accuracy, predictive sensitivity to design parameters, versatility, computational efficiency, the reproducibility of predictions and software usability as well as the models’ transparency (how open they are) and modularity. We also discuss their comprehensiveness. From this evaluation, we conclude that current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and in the modelling of household behaviours relating to investment decisions and energy using practices. There is a need for an open-source and modular dynamic housing stock energy modelling platform that addresses current limitations, can be readily updated as new (e.g. housing survey) calibration data is released and be readily extended by the modelling community at large: improving upon the utilisation of scarce developmental resources. This would represent a considerable step forward in the formulation of housing stock decarbonisation policy that is informed by sound evidence. [ABSTRACT FROM AUTHOR]

Increasing demand for housing is one of the biggest challenges facing the world. Affordable housing is a key priority of the UK government in addressing this challenge, which calls for innovative construction methods to address the issue of fuel poverty at an affordable cost. Timber-based modern methods of construction are one of the key solutions to resolve the existing housing crisis while managing climate change. Therefore, this paper presents a case study of "Integra House", which is a proof of concept of a novel truss technology. The case study is an affordable housing prototype that performs well in both life cycle carbon and cost. The proposed construction uses a novel timber truss technology which makes up the floor, walls and roof of the house, thereby reducing on-site operations and waste while providing a low-carbon low-cost design. The prototype underwent design optimisation and evaluation of options; workshop-based production and performance evaluation of elemental prototypes; production and performance evaluation of a full-scale dwelling prototype; and comparison of capital and life cycle costs and environmental impacts. Further, it underwent a simulation-based optimisation to maximize its performance in cost and carbon by replacing milled timber trusses with whole timber trusses and Rockwool insulation with wood wool insulation. The optimised design option (whole timber) has an EC of 261kgCO 2 per m2 and costs £682 per m2 (excludes substructure, services and fitout) and its operational carbon is 7.9 kgCO 2 per m2/annum and costs £3.30 per m2/annum to operate. Life cycle costs and carbon comparison of the two design prototypes concluded that the whole timber design outperformed the milled timber design in both cost and carbon aspects, by 23% and 30% respectively due to being extremely inexpensive and requiring minimal processing compared to the milled timber option. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of the relationships between dwelling, household, and motivation, behaviour and perception characteristics and winter heating setpoint temperatures (n = 111) and heating periods (n = 148 and 145) used in UK social housing. The work capitalises on primary data from a socio-technical household survey, undertaken in Plymouth, UK, during 2015, which was merged with building audit data collected by the social housing association managing the properties. The mean reported heating setpoint temperature was 20.9 °C and the average weekday and weekend day heating periods were 9.5 h and 11.2 h respectively. The results suggest that heating setpoint temperatures and periods vary greatly among UK social houses, but there are clear systematic variations according to dwelling, household, and motivation, behaviour and perception characteristics. The research could enable social housing providers, the government and commercial organisations to target energy efficiency measures (i.e. thermal upgrades) and social interventions (i.e. behaviour change) at those dwellings and households where their impact may be most beneficial. The results presented could also be used to better inform the assumptions of heating preferences in energy models, which could result in more realistic predictions of the space heating demands of social housing and the potential energy savings from refurbishment measures. [ABSTRACT FROM AUTHOR]

• Domestic energy mapping can rapidly and accurately target appropriate dwellings. • Dwelling-level data collection by local community improved input for energy model. • Multiple routes of targeting using high energy use, fuel poverty and typologies. • Projected 89–94% mean energy reduction using whole house retrofit package. • Funding programmes need to be customised for different household segments. A long-standing challenge for area-based mass retrofits has been the ability to rapidly and accurately target appropriate dwellings for energy improvements. This paper demonstrates the application of a data-driven localised Geographical Information System (GIS)-based domestic energy mapping approach to create house-by-house baseline energy models and predict the potential for whole house energy retrofits in a case study of 431 dwellings in Oxford (UK). Top - down spatial datasets on energy, housing, socioeconomics and fuel poverty are combined with bottom-up energy modelling underpinned by actual dwelling details gathered through questionnaire surveys by the local community group. Multiple routes to identify suitable dwellings were tested such as grouping dwellings with high energy use, those with high levels of fuel poverty and by common dwelling characteristics. About 300 dwellings were found to be suitable for a whole house retrofit package, equating to 89–94% mean energy reduction over baseline. While the most common dwelling typology, 1930s semi-detached had high retrofit need, it fell in an area with low annual household income. The second most common dwelling typology, 1930s terrace, was dominant in areas with median level of household income. Funding programmes will need to be customised for different household segments to increase the take-up of energy retrofits. [ABSTRACT FROM AUTHOR]

This paper describes a new method to swiftly model the dynamics of heating energy demand and indoor air temperatures of houses and housing stocks. The Reduced data Energy Model (RdDEM) provides a cost-effective alternative to steady-state modelling by enhancing the input dataset from the Reduced data Standard Assessment Procedure (RdSAP) – the method used to calculate Energy Performance Certificates (EPC) in the UK. This eliminates the main drawbacks associated with dynamic thermal simulation (DTS) of housing stocks, namely the large amount of required input data and the significant time required to model each house. The RdDEM algorithms create RdSAP-equivalent geometry, construction, thermal mass and boundary conditions in Energy Plus DTS software. The new inferences and methodological enhancements were first tested and then implemented at scale using a sample of 83 semi-detached houses. Most energy results from RdDEM were within 10% of those from RdSAP. The differences are explained by the different ways that indoor air temperature is calculated. The RdDEM method provides a dynamic alternative to RdSAP for understanding the dynamics of energy demand and indoor air temperatures in homes. This could include assessing the peak demand of a community energy scheme or assessing the summertime overheating risk in individual dwellings. Ultimately, it could provide a dynamic housing stock model using the data already collected from millions of houses to generate EPCs. [ABSTRACT FROM AUTHOR]