This model generates a set of representative housing archetypes that will assist large scale domestic modelling in the UK, relevant to energy, thermal comfort and indoor air quality.
Stock modelling can be a powerful tool for building design optimisation on a national level. Performance analysis software requires simplified but generic input (in terms of geometry, fabric, equipment and operational characteristics) that will enable the quick and accurate representation of the housing sector as a whole. This work extends previous work via the production of a rigorous, statistically valid set of archetypes.
The archetypes model is the result of an intensive analysis of the English housing stock that relies on multiple sources of quantitative and qualitative evidence. The model is primarily synthesised by data extracted from the English Housing Survey (EHS) 2010-11. The distributions of key variables (such as built form, floor area, storey height, construction age and fabric characteristics) of the EHS were statistically analysed to identify representative parameters, which were then compared, and complemented where necessary, with information from literature and other databases. Internal layouts were based on typical access patterns from dwellings of corresponding form, size and age. Cluster analysis was used to identify similar groups of parameters, such as constructional details.
This led to the identification of a set of archetypes, broadly representative of the English housing stock. This unique group of generic house types and characteristics can be used as a default data library in housing stock models that examine environmental and health issues to inform policy decisions and encourage uptake of energy efficiency measures.
|Type:||Bottom-up housing stock model|
|Purpose:||Improve large-scale domestic models|
|Spatial scale:||England (can be extended to represent UK as a whole)|
|Main contact:||Eleni Oikonomou|
|Other contacts:||Anna Mavrogianni; Rokia Raslan; Jonathon Taylor; Michael Davies|
Model documentation is being prepared and will be added here when available.