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Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

Author

Listed:
  • Antonio Attanasio

    (Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
    These authors contributed equally to this work.)

  • Marco Savino Piscitelli

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy
    These authors contributed equally to this work.)

  • Silvia Chiusano

    (Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Turin, Italy
    These authors contributed equally to this work.)

  • Alfonso Capozzoli

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy
    These authors contributed equally to this work.)

  • Tania Cerquitelli

    (Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
    These authors contributed equally to this work.)

Abstract

Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance.

Suggested Citation

  • Antonio Attanasio & Marco Savino Piscitelli & Silvia Chiusano & Alfonso Capozzoli & Tania Cerquitelli, 2019. "Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates," Energies, MDPI, vol. 12(7), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1273-:d:219424
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    References listed on IDEAS

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    6. Tania Cerquitelli & Giovanni Malnati & Daniele Apiletti, 2019. "Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings," Energies, MDPI, vol. 12(15), pages 1-18, July.
    7. Sooyoun Cho & Jeehang Lee & Jumi Baek & Gi-Seok Kim & Seung-Bok Leigh, 2019. "Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach," Energies, MDPI, vol. 12(21), pages 1-23, October.
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