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

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Book cover Smart Energy Control Systems for Sustainable Buildings

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 67))

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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.

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Abbreviations

A :

Area

V :

Volume

S/V :

Surface/volume ratio

h :

Characteristic height

w :

Characteristic width

d :

Characteristic depth

S :

Housing stock

a :

Column vector

P :

Parameter

j :

Type

t :

Length

GE:

Google Earth

GSV:

Google Street View

LAH:

Local authority housing

MT:

Mid terrace

ET:

End of terrace

SD:

Semi-detached

RCM:

Remote cataloguing method

RMM:

Remote measurement method

BER:

Building energy rating

DEAP:

Dwelling energy assessment procedure

EM:

Engineering method

SM:

Statistical method

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Authors and Affiliations

  1. Cork Institute of Technology, Architecture, Cork, Ireland

    James Pittam, Paul D. O’Sullivan & Garrett O’Sullivan

Authors
  1. James Pittam
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  2. Paul D. O’Sullivan
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  3. Garrett O’Sullivan
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Corresponding author

Correspondence to James Pittam .

Editor information

Editors and Affiliations

  1. Cardiff School of Art & Design, The Ecological Built Environment group (EBERE), Cardiff Metropolitan University, Cardiff, United Kingdom

    John Littlewood

  2. Bartlett School Env, Energy & Resou, University College London Bartlett School Env, Energy & Resou, London, United Kingdom

    Catalina Spataru

  3. KES International, Shoreham-by-Sea, United Kingdom

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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Pittam, J., O’Sullivan, P.D., O’Sullivan, G. (2017). An Archetype Based Building Stock Aggregation Methodology Using a Remote Survey Technique. In: Littlewood, J., Spataru, C., Howlett, R., Jain, L. (eds) Smart Energy Control Systems for Sustainable Buildings. Smart Innovation, Systems and Technologies, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-319-52076-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-52076-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52074-2

  • Online ISBN: 978-3-319-52076-6

  • eBook Packages: EngineeringEngineering (R0)

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