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Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements

Author

Listed:
  • Pierryves Padey

    (Solar Energy and Building Physics Laboratory, Institute of Thermal Engineering, University of Applied Sciences of Western Switzerland (HES-SO), Avenue de Sports 20, 1401 Yverdon-les-Bains, Switzerland)

  • Kyriaki Goulouti

    (Solar Energy and Building Physics Laboratory, Institute of Thermal Engineering, University of Applied Sciences of Western Switzerland (HES-SO), Avenue de Sports 20, 1401 Yverdon-les-Bains, Switzerland)

  • Guy Wagner

    (Solar Energy and Building Physics Laboratory, Institute of Thermal Engineering, University of Applied Sciences of Western Switzerland (HES-SO), Avenue de Sports 20, 1401 Yverdon-les-Bains, Switzerland)

  • Blaise Périsset

    (Solar Energy and Building Physics Laboratory, Institute of Thermal Engineering, University of Applied Sciences of Western Switzerland (HES-SO), Avenue de Sports 20, 1401 Yverdon-les-Bains, Switzerland)

  • Sébastien Lasvaux

    (Solar Energy and Building Physics Laboratory, Institute of Thermal Engineering, University of Applied Sciences of Western Switzerland (HES-SO), Avenue de Sports 20, 1401 Yverdon-les-Bains, Switzerland)

Abstract

The performance gap, defined as the difference between the measured and the calculated performance of energy-efficient buildings, has long been identified as a major issue in the building domain. The present study aims to better understand the performance gap in high-energy performance buildings in Switzerland, in an ex-post evaluation. For an energy-efficient building, the measured heating demand, collected through a four-year measurement campaign was compared to the calculated one and the results showed that the latter underestimates the real heating demand by a factor of two. As a way to reduce the performance gap, a probabilistic framework was proposed so that the different uncertainties of the model could be considered. By comparing the mean of the probabilistic heating demand to the measured one, it was shown that the performance gap was between 20–30% for the examined period. Through a sensitivity analysis, the active air flow and the shading factor were identified as the most influential parameters on the uncertainty of the heating demand, meaning that their wrong adjustment, in reality, or in the simulations, would increase the performance gap.

Suggested Citation

  • Pierryves Padey & Kyriaki Goulouti & Guy Wagner & Blaise Périsset & Sébastien Lasvaux, 2021. "Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements," Energies, MDPI, vol. 14(19), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6178-:d:645110
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    References listed on IDEAS

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    1. Xing Shi & Binghui Si & Jiangshan Zhao & Zhichao Tian & Chao Wang & Xing Jin & Xin Zhou, 2019. "Magnitude, Causes, and Solutions of the Performance Gap of Buildings: A Review," Sustainability, MDPI, vol. 11(3), pages 1-21, February.
    2. Cozza, Stefano & Chambers, Jonathan & Patel, Martin K., 2020. "Measuring the thermal energy performance gap of labelled residential buildings in Switzerland," Energy Policy, Elsevier, vol. 137(C).
    3. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    4. Kucherenko, S. & Song, S., 2017. "Different numerical estimators for main effect global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 222-238.
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