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Data-Driven Decision-Making Framework for Cost-Efficient Energy Retrofit of Italian Residential Building Stock

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Intelligent Systems and Applications (IntelliSys 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 822))

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Abstract

Strategic decision-making to invest in building stock energy retrofit is a time-consuming task, requiring a significant amount of complex and technical data with high acquisition costs in its conventional form. Besides cost data of various retrofit alternatives, needing unit-based calculations, the monetary value of resulting benefits, like saved energy, should be considered for making cost-efficient decisions by policymakers. On the other hand, novel techniques for building retrofit are gaining increasing attention due to their vital role in sustaining the built environment and lowering energy consumption, especially in countries like Italy, where a significant share of the residential building stock is outdated. With advancements in data-collection methods and data-driven decision support systems, more data is available, based on which these decisions can be automated and optimized. This research aims to provide a comprehensive framework for an automated cost-benefit analysis for various energy retrofit scenarios given their energy-saving potential, implementation cost, and associated investment payback period. Based on Italian National databases, i.e., CENED (Building Energy Certificate), TABULA, and Superbonus Cost data of the Lombardy Region, this study proposes a building stock clustering using energy labels, building technologies, and construction period to identify building archetypes. Moreover, the maximum investment amount of each cluster (archetype) for different payback periods is calculated using Monte Carlo Simulation and is compared to the estimated retrofit cost based on regional price lists for each archetype. This cost-benefit analysis using estimated retrofit cost and saved energy cost contributes to faster, more objective, factual, and cost-optimized decision-making for urban-level energy retrofit decision-making, highlighting the importance of government funding and subsidies like Superbonus in achieving sustainability, energy efficiency, and decarbonization goals of the European Union.

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Authors and Affiliations

  1. Department of Architecture, Politecnico Di Milano, Built Environment and Construction Engineering, Via Ponzio 31, 20133, Milan, Italy

    Ania Khodabakhshian & Fulvio Re Cecconi

Authors
  1. Ania Khodabakhshian
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  2. Fulvio Re Cecconi
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Correspondence to Ania Khodabakhshian .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Khodabakhshian, A., Re Cecconi, F. (2024). Data-Driven Decision-Making Framework for Cost-Efficient Energy Retrofit of Italian Residential Building Stock. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-031-47721-8_35

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