AI-Driven Adaptive Facade Systems in High-Rise Buildings: A Study on Integrating Machine Learning with Real-Time Environmental Data to Optimize Energy Efficiency

Abstract This paper investigates the integration of Artificial Intelligence (AI) into adaptive façade systems in high-rise buildings to improve energy efficiency through real-time reactivity with external conditions. Three façade systems—static, pre-programmed and AI-powered adaptive façade systems—are modelled and evaluated using Rhino and Grasshopper, Ladybug Tools, and OpenStudio, a full simulation environment, and algorithmic optimization techniques. The study assesses the potential of AI-driven façades to outperform both static and pre-programmed adaptive systems under various climatic scenarios by integrating a Machine Learning (ML) model trained using the Extreme Gradient Boosting (XGBoost) algorithm on contextual cross-validated environmental and building performance data. The findings show that AI-enhanced systems provide notable gains in energy efficiency and reactivity, especially under harsh weather.