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Predicting buildings' EPC in Italy: a machine learning based-approach

Author

Listed:
  • Francesco Braggiotti

    (Datasinc)

  • Nicola Chiarini

    (Datasinc)

  • Giulio Dondi

    (Datasinc)

  • Luciano Lavecchia

    (Bank of Italy)

  • Valeria Lionetti

    (Bank of Italy)

  • Juri Marcucci

    (Bank of Italy)

  • Riccardo Russo

    (Bank of Italy)

Abstract

EU member states have committed to achieving carbon neutrality by 2050. Given that building-related activities contribute to almost a quarter of EU greenhouse gas emissions, the implementation of new regulations to decarbonize this sector is paramount. However, policymakers must carefully evaluate policies to mitigate transition risks associated with these regulations, as buildings represent a significant portion of household wealth and bank assets. Accurate metrics regarding buildings' energy efficiency, including the energy class reported in Energy Performance Certificates (EPCs), are essential for such evaluations. In this study, we developed a machine learning-based model to predict the energy class of Italian buildings using publicly accessible data. The model, trained on a geographic subset of the Italian territory, achieves a 37% accuracy rate, which increases to 74% when allowing for a one-class margin of error (1-class accuracy). Further testing against a mortgage portfolio provided by a commercial bank yielded a 69% 1-class accuracy. Comparison with statistics reported by the official EPC register (SIAPE) suggests a potential discrepancy in the representation of the worst energy efficiency class.

Suggested Citation

  • Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024. "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers) 850, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_850_24
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2024-0850/QEF_850_24.pdf
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    References listed on IDEAS

    as
    1. Sun, Maoran & Han, Changyu & Nie, Quan & Xu, Jingying & Zhang, Fan & Zhao, Qunshan, 2022. "Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow," OSF Preprints g8p4f, Center for Open Science.
    2. Peter Reusens & Frank Vastmans & Sven Damen, 2022. "The impact of changes in dwelling characteristics and housing preferences on house price indices," Working Paper Research 406, National Bank of Belgium.
    3. P. Reusens & F. Vastmans & S. Damen, 2022. "The impact of changes in dwelling characteristics and housing preferences on Belgian house prices," Economic Review, National Bank of Belgium, pages 1-40, April.
    4. Mayer, Kevin & Haas, Lukas & Huang, Tianyuan & Bernabé-Moreno, Juan & Rajagopal, Ram & Fischer, Martin, 2023. "Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data," Applied Energy, Elsevier, vol. 333(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    energy performance certificates; EPC; Italy; buildings; transition risk; machine learning; random forest classifier; random forest;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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