Estimating Building Airtightness from Data-A Case Study

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Abstract

The focus on energy conservation in buildings is increasing. Despite that, the yearly building renovation rate is only at around 1 %. To increase the renovation rate, new and time-efficient methods used for screening of large building portfolios' energy saving potential are needed. In this paper, a re-engineered take on the classical energy signature method is applied to two renovated apartments in Denmark. The energy signature model relies on time-series measurements of space heat consumption, outdoor temperature, solar irradiation, and wind speed. The estimates obtained from it consist of-among other things-heat loss coefficient and wind-induced heat loss. This paper focuses on the latter. To validate the model estimate, the airtightness has been quantified by blower door-tests in both apartments: the results showed that one apartment is reasonable airtight, while the other suffers from significant air leakages. The energy signature and two other infiltration models, based on blower door test results, were compared. Good agreement between the results obtained from the data-driven energy signature and the blower door test were found. With use of a simple linear relation between the average infiltration and the blower door test result (q50), from the Danish national building code, the energy signature was found to overestimate the blower door test result (q50) by 33 % for the leaky apartment and underestimate the same air flow by 18 % for the other apartment. Both estimates are within the standard error of the infiltration model in the Danish national building code.

Original languageEnglish
Article number10004
JournalE3S Web of Conferences
Volume246
Number of pages8
ISSN2555-0403
DOIs
Publication statusPublished - 29 Mar 2021
Event2021 Cold Climate HVAC and Energy 2021 - Tallinn, Estonia
Duration: 18 Apr 202121 Apr 2021

Conference

Conference2021 Cold Climate HVAC and Energy 2021
CountryEstonia
CityTallinn
Period18/04/202121/04/2021

Bibliographical note

Funding Information:
Acknowledgments: This work was supported by the project SmartTune founded by The Research Council of Norway (296345); Centre for IT-Intelligent Energy Systems in Cities (CITIES) founded by the Danish Innovation Fund (DSF1305-00027B–CITIES); and Renovating Buildings Sustainably (REBUS) (5151-00002B) which provided the data.

Publisher Copyright:
© The Authors, published by EDP Sciences, 2021.

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