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Addressing Large-Scale Energy Retrofit of a Building Stock via Representative Building Samples: Public and Private Perspectives

Author

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  • Fabrizio Ascione

    (Department of Industrial Engineering, Università degli studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Naples, Italy)

  • Nicola Bianco

    (Department of Industrial Engineering, Università degli studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Naples, Italy)

  • Claudio De Stasio

    (Department of Industrial Engineering, Università degli studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Naples, Italy)

  • Gerardo Maria Mauro

    (Department of Industrial Engineering, Università degli studi di Napoli Federico II, Piazzale Tecchio 80, 80125 Naples, Italy)

  • Giuseppe Peter Vanoli

    (Department of Medicine, Università degli studi del Molise, Via Cesare Gazzani 47, 86100 Campobasso, Italy)

Abstract

Scientific literature about energy retrofit focuses on single buildings, but the investigation of whole building stocks is particularly worthy because it can yield substantial energy, environmental and economic benefits. Hence, how to address large-scale energy retrofit of existing building stocks? The paper handles this issue by employing a methodology that provides a robust energy analysis of building categories. This is denoted as SLABE, “Simulation-based Large-scale uncertainty/sensitivity Analysis of Building Energy performance”. It was presented by the same authors and is here enhanced to investigate a whole and heterogeneous building stock that includes various categories. Each category is represented via a Representative Building Sample (RBS), which is defined through Latin hypercube sampling and uncertainty analysis. Hence, optimal retrofit packages are found in function of building location, intended use and construction type. Two families of optimal solutions are achieved. The first one collects the most energy-efficient (and thus sustainable) solutions, among the ones that produce global cost savings, thereby addressing the public perspective. The second one collects cost-optimal solutions thereby addressing the private perspective. EnergyPlus is employed as a simulation tool and coupled with MATLAB ® for data analysis and processing. The methodology is applied to a significant share of the Italian public administration building stock, which includes several building categories depending on location, use destination and construction type. The outcomes show huge potential energy and economic savings, and could support a deep energy renovation of the Italian building stock.

Suggested Citation

  • Fabrizio Ascione & Nicola Bianco & Claudio De Stasio & Gerardo Maria Mauro & Giuseppe Peter Vanoli, 2017. "Addressing Large-Scale Energy Retrofit of a Building Stock via Representative Building Samples: Public and Private Perspectives," Sustainability, MDPI, vol. 9(6), pages 1-18, June.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:940-:d:100336
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    References listed on IDEAS

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    1. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    2. Fabrizio Ascione & Nicola Bianco & Rosa Francesca De Masi & Gerardo Maria Mauro & Giuseppe Peter Vanoli, 2015. "Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort," Sustainability, MDPI, vol. 7(8), pages 1-28, August.
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    7. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
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    9. Ascione, Fabrizio & Böttcher, Olaf & Kaltenbrunner, Robert & Vanoli, Giuseppe Peter, 2017. "Methodology of the cost-optimality for improving the indoor thermal environment during the warm season. Presentation of the method and application to a new multi-storey building in Berlin," Applied Energy, Elsevier, vol. 185(P2), pages 1529-1541.
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    Cited by:

    1. Juan Aranda & Ignacio Zabalza & Andrea Conserva & Gema Millán, 2017. "Analysis of Energy Efficiency Measures and Retrofitting Solutions for Social Housing Buildings in Spain as a Way to Mitigate Energy Poverty," Sustainability, MDPI, vol. 9(10), pages 1-22, October.
    2. Nikolaos Papadakis & Dimitrios Al. Katsaprakakis, 2023. "A Review of Energy Efficiency Interventions in Public Buildings," Energies, MDPI, vol. 16(17), pages 1-34, August.
    3. Fahlstedt, Oskar & Temeljotov-Salaj, Alenka & Lohne, Jardar & Bohne, Rolf André, 2022. "Holistic assessment of carbon abatement strategies in building refurbishment literature — A scoping review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Waqas Ahmed Mahar & Griet Verbeeck & Sigrid Reiter & Shady Attia, 2020. "Sensitivity Analysis of Passive Design Strategies for Residential Buildings in Cold Semi-Arid Climates," Sustainability, MDPI, vol. 12(3), pages 1-22, February.
    5. Dimitris A. Katsaprakakis & Nikos Papadakis & Efi Giannopoulou & Yiannis Yiannakoudakis & George Zidianakis & Michalis Kalogerakis & George Katzagiannakis & Eirini Dakanali & George M. Stavrakakis & A, 2023. "Rational Use of Energy in Sports Centres to Achieve Net Zero: The SAVE Project (Part A)," Energies, MDPI, vol. 16(10), pages 1-41, May.
    6. Peep Pihelo & Kalle Kuusk & Targo Kalamees, 2020. "Development and Performance Assessment of Prefabricated Insulation Elements for Deep Energy Renovation of Apartment Buildings," Energies, MDPI, vol. 13(7), pages 1-20, April.
    7. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    8. Thrampoulidis, Emmanouil & Hug, Gabriela & Orehounig, Kristina, 2023. "Approximating optimal building retrofit solutions for large-scale retrofit analysis," Applied Energy, Elsevier, vol. 333(C).

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