An Archetype Based Building Stock Aggregation Methodology Using a Remote Survey Technique

Abstract

Developing representative archetypes using a bottom up approach for stock modelling is an excellent tool for evaluating the overall performance of the building stock; however it requires detailed analysis of the various building types. Currently there is no detailed housing data base for Local Authority housing in Ireland that catalogues the housing stock according to geometric configuration and thermal characteristics for each typology. The aim of this chapter is to present a methodology to catalogue LAH stock and build a detailed housing stock data base. The GIS web based mapping application Google Street View is used to identify 18 house typologies across 36 LAH developments for Cork City in the South of Ireland, used as a dataset for demonstration of the methodology; a total of 10,318 housing units are counted and information subdivided into end of terrace, mid terrace, semi-detached, terrace lengths, orientation and elevation. This database then provides the base line assessment for building a stock aggregation model; the stock aggregation approach is used as a method to evaluate the energy performance of the building stock, beginning with analysis of individual house types; referred to as a ‘bottom up approach’. Four representative archetypes are produced from the study and subsequently modelled using DEAP simulation software. 10,318 homes were identified, catalogued and statistically analysed within a number of weeks for the city council. This can be extended nationally very effectively with data bases now being constructed remotely rather than the challenges of physical mapping and surveying. Using a matrix based linear system for weighting parameters allows a large number of computationally efficient transformations of house types and parameters into archetypes. There is also flexibility in determining the grouping algorithm depending on the nature of the LAH studied. Investment in retrofit is highly justified in this area with large potential for reducing CO2 emissions, the number of fuel poverty sufferers and victims of seasonal mortality due to thermally inefficient homes. The study suggests the method applied has scalable potential and is modular in structure facilitating wider adaptation.