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Learning from experts: Energy efficiency in residential buildings

Author

Listed:
  • Billio, Monica
  • Casarin, Roberto
  • Costola, Michele
  • Veggente, Veronica

Abstract

Reducing energy consumption is a key policy focus for mitigating climate change. This study investigates the determinants of residential building energy efficiency, leveraging expert insights from Energy Performance Certificates (EPCs) to develop a machine learning prediction framework. Datasets from countries at distinct latitudes, the UK and Italy, are analyzed to identify potential regional variations in the factors influencing energy efficiency. Findings reveal the crucial role of factors related to heating systems and insulation materials in the determination of the building’s efficiency. Also, there is evidence of the superior ability of non-linear machine learning models to capture complex relationships between building characteristics and efficiency. A scenario analysis further demonstrates the cost-effectiveness of policies informed by machine learning recommendations.

Suggested Citation

  • Billio, Monica & Casarin, Roberto & Costola, Michele & Veggente, Veronica, 2024. "Learning from experts: Energy efficiency in residential buildings," Energy Economics, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:eneeco:v:136:y:2024:i:c:s014098832400358x
    DOI: 10.1016/j.eneco.2024.107650
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    References listed on IDEAS

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    More about this item

    Keywords

    Energy efficiency; Energy performance certificate; Machine learning; Tree-based models; Big data;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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