View in EDS

BIM-Based Machine Learning Framework for Early-Stage Building Energy Performance Prediction.

Saved in:
Bibliographic Details
Title: BIM-Based Machine Learning Framework for Early-Stage Building Energy Performance Prediction.
Authors: de Paula, Liliane Magnavaca, Oloufa, Amr, Tatari, Omer
Source: Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 1, p320, 22p
Subject Terms: BUILDING information modeling, SUSTAINABLE design, MACHINE learning, PREDICTION models, DATA analysis, BUILDING design & construction, ENERGY consumption, EMPIRICAL research
Abstract: Featured Application: The proposed BIM-Machine Learning framework applies to early-stage assessment of building energy performance, providing an expeditious data-driven approach to support sustainable design decision-making. A Building Information Modeling (BIM)-based Machine Learning (ML) framework was developed to predict the energy performance of office buildings at the early design stage. The framework provides a reproducible and data-driven workflow that shortens simulation time while maintaining accuracy. Revit and Insight were integrated with statistical modeling in Weka to create an automated and regionally adaptable process derived from BIM-generated data. A reduced-factorial Design of Experiments (DOE) guided the generation of 210 parametric simulations representing base, generalization, and stress-test models for Orlando, Florida. Each model combined geometric, envelope, system, and operational variations, forming a dataset of 14 independent parameters and two dependent energy metrics: Energy Use Intensity (EUI) and Operational Energy (OE). Four regression algorithms—Linear Regression (LR), M5P, SMOReg, and Random Forest (RF)—were trained and validated through 10-fold cross-validation. All models achieved R2 values above 0.95, with the RF model reaching the highest overall accuracy under default parameter settings, with R2 > 0.97 and mean absolute errors below 5% across both metrics, EUI and OE. Feature-importance analysis identified HVAC system type, window-to-wall ratio, and operational schedule as the most influential variables. Results confirm that BIM-ML integration enables rapid and reliable energy-performance prediction, supporting informed, energy-efficient design decisions in the earliest phases of the building lifecycle. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:Featured Application: The proposed BIM-Machine Learning framework applies to early-stage assessment of building energy performance, providing an expeditious data-driven approach to support sustainable design decision-making. A Building Information Modeling (BIM)-based Machine Learning (ML) framework was developed to predict the energy performance of office buildings at the early design stage. The framework provides a reproducible and data-driven workflow that shortens simulation time while maintaining accuracy. Revit and Insight were integrated with statistical modeling in Weka to create an automated and regionally adaptable process derived from BIM-generated data. A reduced-factorial Design of Experiments (DOE) guided the generation of 210 parametric simulations representing base, generalization, and stress-test models for Orlando, Florida. Each model combined geometric, envelope, system, and operational variations, forming a dataset of 14 independent parameters and two dependent energy metrics: Energy Use Intensity (EUI) and Operational Energy (OE). Four regression algorithms—Linear Regression (LR), M5P, SMOReg, and Random Forest (RF)—were trained and validated through 10-fold cross-validation. All models achieved R<sup>2</sup> values above 0.95, with the RF model reaching the highest overall accuracy under default parameter settings, with R<sup>2</sup> > 0.97 and mean absolute errors below 5% across both metrics, EUI and OE. Feature-importance analysis identified HVAC system type, window-to-wall ratio, and operational schedule as the most influential variables. Results confirm that BIM-ML integration enables rapid and reliable energy-performance prediction, supporting informed, energy-efficient design decisions in the earliest phases of the building lifecycle. [ABSTRACT FROM AUTHOR]
ISSN:20763417
DOI:10.3390/app16010320