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This paper proposes a framework that integrates 6D Building Information Modeling (6D BIM) with machine learning techniques to predict building energy consumption under different climate scenarios. Firstly, a machine learning model is trained using the dataset generated from the 6D BIM-based parametric study. Next, a regression model is constructed based on the simulation results with the weather data according to climate change scenarios RCP 2.6, RCP 4.5 and RCP 8.5. Combining two models allows for the forecasting of future building energy demand up to the year 2100. A case study of 2-storey private house in Hanoi is presented to illustrate the proposed framework. This research underscores the potential of advanced digital tools and data-driven methods to support building design and operation in an era of environmental uncertainty.

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Predicting Building Energy Consumption Considering Climate Change Using 6D BIM and Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Tran-Hieu Nguyen,
  • Dung Do Thi Mai

摘要

This paper proposes a framework that integrates 6D Building Information Modeling (6D BIM) with machine learning techniques to predict building energy consumption under different climate scenarios. Firstly, a machine learning model is trained using the dataset generated from the 6D BIM-based parametric study. Next, a regression model is constructed based on the simulation results with the weather data according to climate change scenarios RCP 2.6, RCP 4.5 and RCP 8.5. Combining two models allows for the forecasting of future building energy demand up to the year 2100. A case study of 2-storey private house in Hanoi is presented to illustrate the proposed framework. This research underscores the potential of advanced digital tools and data-driven methods to support building design and operation in an era of environmental uncertainty.