Abstract

Average reference dwellings representing a predominant housing typology are defined in this work. Specifying such reference buildings is a prerequisite for (i) calculating cost-optimal energy performance requirements for buildings and building elements and (ii) ensuring valid calculations of national building energy consumption. In the EU, an Energy Performance Certificate (EPC) rating is an assessment of the energy consumption of a dwelling. The use of inappropriate default-values for the building envelope thermal transmittance coefficients (U-values) and standardised thermal bridging transmittance coefficients (Y-values) in the production of EPCs leads to an over-estimation of potential energy savings from interventions in the existing dwelling stock. A methodology is presented for the derivation of simplified default-free inputs to a bottom-up residential cost-optimality energy consumption model from an EPC dataset. 35 reference dwellings (RDs) are employed to appropriately characterise 406,918 dwellings. Use of these RDs enable quantification of (i) the energy saving potential of a predominant housing typology, (ii) the effect of default U-value and standardised Y-value use on the prebound effect in dwellings (iii) overall national building energy consumption.

Highlights

  • 1.1 Policy ContextHouseholds consume % of end-use energy in the EU [1]

  • Average reference dwellings representing a predominant housing typology are defined in this work

  • In the EU, an Energy Performance Certificate (EPC) rating is an assessment of the energy consumption of a dwelling

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Summary

Introduction

1.1 Policy ContextHouseholds consume % of end-use energy in the EU [1]. The extent and duration of the dominance of the thermal characteristics of pre-existing houses depends on the construction rate, floor areas and specifications of new dwellings [2]. There are few large-scale building monitoring projects [11,12,13], in the small samples of buildings studied [9, 11], evidence of patterns in energy demand in buildings by population and stock segmentations are limited [9, 11, 12, 14, 15], with little common [9, 16], transparent or prescribed data reported [9, 11, 12] This absence of robust data inhibits the effectiveness of policy frameworks [11, 17, 18]. Evidence-based policies are a prerequisite to achieving targets for reduced building energy demand [11,12,13,14, 19,20,21,22]

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