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Predicting Energy Savings of the UK Housing Stock under a Step-by-Step Energy Retrofit Scenario towards Net-Zero

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  • Amar Bennadji

    (Scott Sutherland School of Architecture and Built Environment, Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen AB10 7QB, UK)

  • Mohammed Seddiki

    (Scott Sutherland School of Architecture and Built Environment, Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen AB10 7QB, UK)

  • Jamal Alabid

    (Scott Sutherland School of Architecture and Built Environment, Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen AB10 7QB, UK)

  • Richard Laing

    (Scott Sutherland School of Architecture and Built Environment, Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen AB10 7QB, UK)

  • David Gray

    (Cultural and Creative Business School, Robert Gordon University, Garthdee House, Garthdee Road, Aberdeen AB10 7QB, UK)

Abstract

The UK has one of the least energy-efficient housing stocks in Europe. By 2030, the emissions from UK homes need to fall by at least 24% from 1990 levels to meet the UK’s ambitious goal, which is reaching net-zero emissions. The originality of this paper is to apply the building typology approach to predict energy savings of the UK housing stock under a step-by-step energy retrofit scenario, targeting the Passive House Standard for refurbishments of existing buildings, namely the EnerPHit “Quality-Approved Energy Retrofit with Passive House Components.” The typologies consist of twenty reference buildings, representative of five construction ages and four building sizes. The energy balance of the UK residential buildings was created and validated against statistical data. A building stock retrofit plan specifying the order in which to apply energy efficiency measures was elaborated, and energy savings were calculated. The predicted total energy demand for the UK residential building stock for the year 2022 is 37.7 MTOE, and the carbon emissions estimation is 65.33 MtCO 2 e. The energy-saving potential is 87%, and carbon reductions are about 76%, considering all the steps of renovation applied. It has been demonstrated that the step that provides the biggest savings across the housing stock is the one that involves replacing windows, draught-proofing, and installing mechanical ventilation with heat recovery.

Suggested Citation

  • Amar Bennadji & Mohammed Seddiki & Jamal Alabid & Richard Laing & David Gray, 2022. "Predicting Energy Savings of the UK Housing Stock under a Step-by-Step Energy Retrofit Scenario towards Net-Zero," Energies, MDPI, vol. 15(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3082-:d:800112
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    References listed on IDEAS

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    1. Ahern, Ciara & Griffiths, Philip & O'Flaherty, Micheál, 2013. "State of the Irish housing stock—Modelling the heat losses of Ireland's existing detached rural housing stock & estimating the benefit of thermal retrofit measures on this stock," Energy Policy, Elsevier, vol. 55(C), pages 139-151.
    2. Ballarini, Ilaria & Corrado, Vincenzo & Madonna, Francesco & Paduos, Simona & Ravasio, Franco, 2017. "Energy refurbishment of the Italian residential building stock: energy and cost analysis through the application of the building typology," Energy Policy, Elsevier, vol. 105(C), pages 148-160.
    3. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    4. Maia, Iná & Kranzl, Lukas & Müller, Andreas, 2021. "New step-by-step retrofitting model for delivering optimum timing," Applied Energy, Elsevier, vol. 290(C).
    5. Ilaria Ballarini & Vincenzo Corrado, 2017. "A New Methodology for Assessing the Energy Consumption of Building Stocks," Energies, MDPI, vol. 10(8), pages 1-22, July.
    6. Mata, Érika & Sasic Kalagasidis, Angela & Johnsson, Filip, 2013. "Energy usage and technical potential for energy saving measures in the Swedish residential building stock," Energy Policy, Elsevier, vol. 55(C), pages 404-414.
    7. Di Turi, Silvia & Stefanizzi, Pietro, 2015. "Energy analysis and refurbishment proposals for public housing in the city of Bari, Italy," Energy Policy, Elsevier, vol. 79(C), pages 58-71.
    8. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
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    Cited by:

    1. Łukasz Mazur & Anna Bać & Magdalena Daria Vaverková & Jan Winkler & Aleksandra Nowysz & Eugeniusz Koda, 2022. "Evaluation of the Quality of the Housing Environment Using Multi-Criteria Analysis That Includes Energy Efficiency: A Review," Energies, MDPI, vol. 15(20), pages 1-24, October.

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