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

Panagiotis Tsikas, Athanasios Chassiakos (), Vasileios Papadimitropoulos and Antonios Papamanolis
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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

Energies, 2025, vol. 18, issue 1, 1-24

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.

Keywords: building; energy; building information modeling; building energy modeling; machine learning; random forest; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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