Climate and performance-driven architectural floorplan optimization using deep graph networks

Purpose This study introduces a novel approach to generating and optimizing energy-efficient and climate-responsive architectural floorplans. Design/methodology/approach The DGraph-cGAN model utilizes advanced deep-learning techniques to produce diverse, realistic layouts that meet specific design constraints and functional requirements. Findings The results show significant energy savings (32.1% overall) across different building types and climate conditions, with reductions in energy use intensity, CO2 emissions and annual energy costs. Case studies demonstrate notable improvements in energy savings, CO2 emission reduction, daylight autonomy, thermal comfort and cost savings. Practical implications The DGraph-cGAN model has great potential for advancing architectural design optimization, with opportunities for further refinement and application in various contexts. Originality/value This study contributes to developing a novel approach to optimizing architectural floorplans using deep learning techniques. It provides a valuable tool for architects and designers to create energy-efficient, climate-responsive buildings.