BIM-driven energy performance simulation for smart and sustainable retrofitting of aging structures

ABSTRACT Increasing the demand for efficient energy usage in buildings has urged the use of Building Information Modeling (BIM)-oriented simulation methods to streamline energy optimization. Conventional assessment approaches suffer from low accuracy caused by static nature and less capability of predictive estimations. The present work intends to propose an enhanced BIM-based energy performance simulation process supported by ensemble learning for boosting predictability toward effective retrofitting of aging buildings to be more sustainable. Creating a successful predictive model that combines numerous machine learning methods is the primary objective of this study to enhance the accuracy of energy consumption forecasting. The approach employs a hybrid ensemble model that improves prediction accuracy by fusing Extreme Gradient Boosting (XGBoost) and Random Forest (RF). The BDG2 dataset with hourly energy consumption records and metadata of different building types is employed for model testing and training. Python machine learning libraries like Scikit-Learn and XGBoost are used in deployment. Leveraging the respective strengths of the two models, the introduced ensemble approach is capable of well identifying nonlinear patterns in energy use, reducing overfitting, and enhancing generalizability for different building types. Experimental outcomes show that the developed ensemble model performs 94.1% accuracy, 93.5% precision, 92.8% recall, and 93.1% F1-score. Results show an outstanding improvement over standalone models with consistent predictions for enhanced building energy efficiency. Results confirm the success of combining BIM with machine learning-based energy performance analysis as a scalable effective means of sustainable retrofitting.