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BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance

Author

Listed:
  • Panagiotis Tsikas

    (Department of Civil Engineering, University of Patras, 26500 Patras, Greece)

  • Athanasios Chassiakos

    (Department of Civil Engineering, University of Patras, 26500 Patras, Greece)

  • Vasileios Papadimitropoulos

    (Department of Civil Engineering, University of Patras, 26500 Patras, Greece)

  • Antonios Papamanolis

    (Department of Civil Engineering, University of Patras, 26500 Patras, Greece)

Abstract

The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values.

Suggested Citation

  • Panagiotis Tsikas & Athanasios Chassiakos & Vasileios Papadimitropoulos & Antonios Papamanolis, 2025. "BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance," Energies, MDPI, vol. 18(1), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:1:p:201-:d:1560649
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    References listed on IDEAS

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    5. Khajavi, Hamed & Rastgoo, Amir, 2023. "Improving the prediction of heating energy consumed at residential buildings using a combination of support vector regression and meta-heuristic algorithms," Energy, Elsevier, vol. 272(C).
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