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Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand

Author

Listed:
  • Claudio Nägeli

    (Architecture and Civil Engineering Department, Chalmers University of Technology, 412 96 Gothenburg, Sweden)

  • Liane Thuvander

    (Architecture and Civil Engineering Department, Chalmers University of Technology, 412 96 Gothenburg, Sweden)

  • Holger Wallbaum

    (Architecture and Civil Engineering Department, Chalmers University of Technology, 412 96 Gothenburg, Sweden)

  • Rebecca Cachia

    (Codema-Dublin’s Energy Agency, D02 TK74 Dublin, Ireland)

  • Sebastian Stortecky

    (AIT Austrian Institute of Technology, 1210 Vienna, Austria)

  • Ali Hainoun

    (AIT Austrian Institute of Technology, 1210 Vienna, Austria)

Abstract

Buildings are responsible for around 30 to 40% of the energy demand and greenhouse gas (GHG) emissions in European countries. Building stock energy models (BSEMs) are an established method to assess the energy demand and environmental impact of building stocks. Spatial analysis of building stock energy demand has so far been limited to cases where detailed, building specific data is available. This paper introduces two approaches of using synthetic building stock energy modelling (SBSEM) to model spatially distributed synthetic building stocks based on aggregate data. The two approaches build on different types of data that are implemented and validated for two separate case studies in Ireland and Austria. The results demonstrate the feasibility of both approaches to accurately reproduce the spatial distribution of the building stocks of the two cases. Furthermore, the results demonstrate that by using a SBSEM approach, a spatial analysis for building stock energy demand can be carried out for cases where no building level data is available and how these results may be used in energy planning.

Suggested Citation

  • Claudio Nägeli & Liane Thuvander & Holger Wallbaum & Rebecca Cachia & Sebastian Stortecky & Ali Hainoun, 2022. "Methodologies for Synthetic Spatial Building Stock Modelling: Data-Availability-Adapted Approaches for the Spatial Analysis of Building Stock Energy Demand," Energies, MDPI, vol. 15(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6738-:d:915482
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    References listed on IDEAS

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    Cited by:

    1. Ke Liu & Jianglan Tian & Jianping Chen & Yueming Wen, 2022. "Low-Carbon Retrofitting Path of Existing Public Buildings: A Comparative Study Based on Green Building Rating Systems," Energies, MDPI, vol. 15(22), pages 1-24, November.

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