The construction sector accounts for around 40% of global energy usage and surpasses 36% of carbon emissions, highlighting the urgent need for improved renovation strategies. This research presents an AI-enabled generative design optimization framework that facilitates concurrent multi-objective optimization of architectural design, structural efficiency, and energy performance. The framework employs a 20-variable parametric design space and integrates a hybrid NSGA-III, a reference-point-based many-objective evolutionary algorithm with particle swarm optimization. Machine-learning surrogate models accelerate physics-based simulations by 500–850 times while maintaining prediction accuracy above 95%. The framework is validated through twelve renovation case studies comprising eleven residential and one office building spanning seven European countries, sourced from IEA SHC Task 37 and Passivhaus Institut databases, and calibrated to ASHRAE Guideline 14 standards (CVRMSE ≤ 18.6% across all buildings). The results evidence average reductions of 84.7% in operational energy consumption and enhancements of 20.1% in material efficiency, while consistently attaining a net-zero annual energy balance. Climate conditions significantly influence optimal insulation requirements, with a 37% difference between continental and Mediterranean regions. This study presents a scalable and computationally efficient method for AI-driven renovation design, overcoming the constraints of sequential approaches and facilitating substantial decarbonization of the built environment.
Amro et al. (2026) studied this question.