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A Portuguese approach to define reference buildings for cost-optimal methodologies

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  • Brandão de Vasconcelos, Ana
  • Pinheiro, Manuel Duarte
  • Manso, Armando
  • Cabaço, António

Abstract

The Energy Performance of Buildings Directive (EPBD) recast imposed on all Member States to establish representative reference buildings as an important step in drawing up a methodology for calculating cost-optimal levels of minimum energy performance requirements for buildings and building elements. In this paper, the concept of “reference buildings” and the methodologies used to characterize it are defined. A methodology for defining reference buildings in the national building stock is also proposed. This methodology consists essentially of the creation of a virtual building based on statistical data and, exceptionally, on experts’ knowledge and other sources of information. The parameters necessary for distinguishing samples of buildings and the detailed parameters for characterizing each building are defined and listed. These parameters make it possible to know the most relevant information to be drawn up for each of the buildings and how to standardize the model of data collection for all the existing reference buildings for all categories in the EU. The proposed methodology meets the needs and the existing lack of information in the definition of reference buildings in Portugal. This methodology was successfully implemented so as to define a reference building representative of the residential buildings constructed in Lisbon between 1961 and 1990.

Suggested Citation

  • Brandão de Vasconcelos, Ana & Pinheiro, Manuel Duarte & Manso, Armando & Cabaço, António, 2015. "A Portuguese approach to define reference buildings for cost-optimal methodologies," Applied Energy, Elsevier, vol. 140(C), pages 316-328.
    • Handle: RePEc:eee:appene:v:140:y:2015:i:c:p:316-328
    DOI: 10.1016/j.apenergy.2014.11.035
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    8. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
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