Magnitude and extent of building fabric thermal performance gap in UK low energy housing

Abstract

• Air-permeability, U-value and whole house heat loss data were statistically tested.• Building fabric thermal performance gap was widespread in low energy dwellings.• Airtightness gap was trivial in Passivhaus but significant in non-Passivhaus units.• Gap increased by 0.8 m 3 /h/m 2 for every 1 m 3 /h/m 2 decrease in design air permeability.• Building regulations should require in-situ tests to reduce fabric performance gap. ARTICLE INFO Keywords:Building fabric Low energy dwellings Passivhaus Airtightness Heat loss Thermal transmittance Co-heating test ABSTRACT This paper presents new evidence from a nationwide cross-project meta-study investigating the magnitude and extent of the difference between designed and measured thermal performance of the building fabric of 188 low energy dwellings in the UK. The dataset was drawn from the UK Government's national Building Performance Evaluation programme, and comprises 50 Passivhaus (PH) and 138 non-Passivhaus (NPH) dwellings, covering different built forms and construction systems. The difference between designed and measured values of air permeability (AP), external wall/roof thermal transmittance (U-value) and whole house heat loss were statistically analysed, along with a review of thermal imaging data to explain any discrepancies. The results showed that fabric thermal performance gap was widespread especially in terms of AP, although the magnitude of underperformance was much less in PH dwellings. While measured AP had good correlation with measured space heating energy for PH dwellings, there was no relationship between the two for NPH dwellings. The regression analysis indicated that for every 1 m 3 /h/m 2 reduction in designed air permeability, the gap increased by 0.8 m 3 /h/m 2 @50 Pa. Monte Carlo analysis showed that likelihood of AP gap was 78% in NPH dwellings designed to 5 m 3 /h/m 2 @50 Pa or lower. The study provides useful evidence for improving the fabric thermal performance of new housing through in-situ testing.

Evaluating the influence of building fabric, services and occupant related factors on the actual performance of low energy social housing dwellings in UK

“…• As highlighted by the review of fabric performance, robust detailing of joints, junctions and thresholds should be carefully followed during design and construction stages. Weaknesses in thermal performance of building fabric can be picked up using a combination of diagnostic techniques especially for early detection of problems (Gupta and Kotopouleas, 2018).…”

Integrated Testing of Building Fabric Thermal Performance for Calibration of Energy Models of Three Low-Energy Dwellings in the UK

“…The studies demonstrate the prevalence of performance gap but also demonstrate an inconsistency in build quality. Interestingly Passivhaus dwellings have a much smaller difference between predicted and measured heat loss, thermal transmittance, and AP [34,41]. This is possibly because the designers and builders are more dedicated to the success in performance of the dwellings due to the stringent parameters of the Passivhaus standard.…”

“…In the UK, the Innovate UK-funded BPE Programme led to large-scale in situ measurement of building fabric thermal performance of new-build housing although most of the tests conducted were one-offs [28][29][30][31][32][33]. A cross-project meta-study [34] of mostly projects in the BPE programme statistically analysed the building fabric thermal performance data from 188 new-build low-energy dwellings and found that the probability of a performance gap was highest in AP testing. The gap in whole house heat loss testing (co-heating) and thermal transmittance testing was much smaller and often within expectations.…”

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

“…The characteristics reported are generally inconsistent across the studies due to differences in research foci and data availability issues. The most reported characteristics are the number of people [9,10,15,26,33,49,59,60,[79][80][81], age [9,10,15,17,21,26,33,59,77,80,81], household composition [6,7,12,14,17,30,59,77,80,82,83], and income [9,10,21,26,29,33,79,80]. Additional characteristics reported were ownership status [9,10,26,79,80,84] and education levels [17,21,…”

“…Predicted energy use data are based on standard assessment procedures (SAPs), (dynamic) simulations, or taken from existing databases. SAPs are either based on general assessment tools (e.g., the Passive House Planning Package, PHPP) [77] or national standards, such as the French [19], British [14,23,30,35,93], Belgian [10], Dutch [33,71], Swiss [89,95], Danish [75], Spanish [47], South African [23], or German [15,37,49,56,87] standards. The vast majority of SAPs provide energy estimates in the form of energy use intensities (e.g., in kWh•m −2 •a −1 ) per building type and characteristics, or, in the case of certification-based standards (e.g., [33]), per certification level.…”