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

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.