Abstract
Building performance simulations (BPS) are essential for managing rising energy demands but are computationally intensive, limiting their use in early-stage design. Surrogate models (SMs) offer fast approximations using machine learning but lack generalizability for changing building morphologies. This study introduces three morphology-based feature extraction methods - Zernike image moments, polar raycasting, and principal component analysis - to train a generalizable SM for predicting annual energy use intensity (EUI) of 2.5D residential buildings in Singapore. The best SM achieved a 9.2% RMSPE, with features yielding low error and avoiding overfitting. This framework enables efficient design space exploration, aiding architects and urban designers in optimizing masterplans for energy efficiency. In the final section, we demonstrate a design space exploration (DSE) framework with our generalizable SM using a novel parametric model, showcasing its speed, adaptability and convenience over conventional optimization workflows.HighlightsNovel approach explores building morphology's impact to annual EUI .Using Zernike moments, ray casting and PCA to extract features from any 2.5D buildings.4000 tuned SMs trained from hyperparameter optimisation.Most robust SM achieved 9.2% RMSPE when tested with 800 samples.SM's generalizability is proven by applying it to a design model with previously unseen parameters.