AI-DRIVEN PARAMETRIC FACADE DESIGN FOR ADAPTIVE ARCHITECTURAL PERFORMANCE

Abstract

The implementation of AI parametric facade design counters business in an unprecedented manner, accommodating building flexibility, energy efficiency, and user comfort. This study investigates: the performance of AI-driven parametric facades in enhancing adaptive architectural performance through real-time optimization of energy efficiency and thermal comfort. The facade is AI-driven, using a combination of genetic algorithms and artificial neural networks, to dynamically respond to environmental conditions and minimize ventilation, and air conditioning (HVAC) and lighting energy use while improving indoor climate stability. The testing through simulation demonstrates that facades AI-optimized outperform static systems by far, with higher energy savings, reduced indoor temperature swings, and improved comfort of the occupants. Sensitivity analysis also corroborated that AI-based facades could be responsive under different climate scenarios, thus assuring lasting sustainability and resilience. The conclusions drawn are align with existing intelligent facade control system literature and thereby position the data-driven architecture as a pathway to net-zero energy buildings (NZEBs). Future directions of research will involve hybrid AI approaches with smart building management systems (BMS) integration and real-life application, thereby enhancing facade performance. Through AI adaptive facade design, architects and engineers can facilitate the construction of energy-efficient, climate-sensitive, and sustainable built environments.