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Energy Savings after Comprehensive Renovations of the Building: A Case Study in the United Kingdom and Italy

Author

Listed:
  • Olman Araya Mejías

    (Energy and Fuels Department, Universidad Politécnica de Madrid (UPM), Ríos Rosas 21, 28003 Madrid, Spain)

  • Cristina Montalvo

    (Energy and Fuels Department, Universidad Politécnica de Madrid (UPM), Ríos Rosas 21, 28003 Madrid, Spain)

  • Agustín García-Berrocal

    (Energy and Fuels Department, Universidad Politécnica de Madrid (UPM), Ríos Rosas 21, 28003 Madrid, Spain)

  • María Cubillo

    (Superior Technical School of Buildings, Universidad Politécnica de Madrid (UPM), Avenida Juan de Herrera 6, 28040 Madrid, Spain)

  • Daniel Gordaliza

    (Sinceo2 Consultora Energética, Calle Arte 21, 28033 Madrid, Spain)

Abstract

The housing sector is one of the largest energy consumers in the world. There is an urgent need to renovate the housing stock of existing buildings. Therefore, it is necessary to correctly calculate the energy savings that can be obtained in a renovation project. The correct collection of energy data, the main variables that affect consumption, and people’s usage habits are fundamental elements to quantify the success or consequences that occur in an energy efficiency project. This research study quantifies the results of the energy savings of the European project DREEAM (District Scale Renovation for Energy Efficiency and Market Uptake). This article aims to facilitate the calculation of energy savings with mathematical linear regression models in two different climatic zones in Europe. Furthermore, it aims to improve the calculation of energy savings with mathematical models based on energy data and variables that affect consumption before and after renovations. The variables used for the calculation are hours of use, degree days, and reading days. Tenant behavior has been found to play an important role in actual measured savings. Additionally, the energy consumption patterns of the tenants are different after the renovations.

Suggested Citation

  • Olman Araya Mejías & Cristina Montalvo & Agustín García-Berrocal & María Cubillo & Daniel Gordaliza, 2021. "Energy Savings after Comprehensive Renovations of the Building: A Case Study in the United Kingdom and Italy," Energies, MDPI, vol. 14(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6460-:d:652520
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    References listed on IDEAS

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    1. Piotr F. Borowski, 2021. "Digitization, Digital Twins, Blockchain, and Industry 4.0 as Elements of Management Process in Enterprises in the Energy Sector," Energies, MDPI, vol. 14(7), pages 1-20, March.
    2. S´ebastien Houde & Tobias Wekhof, 2021. "The Narrative of the Energy Efficiency Gap," CER-ETH Economics working paper series 21/359, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    3. Krarti, Moncef, 2015. "Evaluation of large scale building energy efficiency retrofit program in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1069-1080.
    4. A. Greening, Lorna & Greene, David L. & Difiglio, Carmen, 2000. "Energy efficiency and consumption -- the rebound effect -- a survey," Energy Policy, Elsevier, vol. 28(6-7), pages 389-401, June.
    5. Baležentis, Tomas & Butkus, Mindaugas & Štreimikienė, Dalia & Shen, Zhiyang, 2021. "Exploring the limits for increasing energy efficiency in the residential sector of the European Union: Insights from the rebound effect," Energy Policy, Elsevier, vol. 149(C).
    6. Böhringer, Christoph & Rivers, Nicholas, 2021. "The energy efficiency rebound effect in general equilibrium," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    7. Haas, Reinhard & Biermayr, Peter, 2000. "The rebound effect for space heating Empirical evidence from Austria," Energy Policy, Elsevier, vol. 28(6-7), pages 403-410, June.
    8. Kelly Kissock, J. & Eger, Carl, 2008. "Measuring industrial energy savings," Applied Energy, Elsevier, vol. 85(5), pages 347-361, May.
    9. Piotr F. Borowski, 2020. "Zonal and Nodal Models of Energy Market in European Union," Energies, MDPI, vol. 13(16), pages 1-21, August.
    10. Yujie Xu & Vivian Loftness & Edson Severnini, 2021. "Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio," Energies, MDPI, vol. 14(14), pages 1-24, July.
    11. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    12. Trainer, Ted, 2017. "Some problems in storing renewable energy," Energy Policy, Elsevier, vol. 110(C), pages 386-393.
    13. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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