Tabatabaei Manesh, M., Rajaian Hoonejani, M., Ghafari Gousheh, S., Abdolmaleki, A., Nikkhah Dehnavi, A., & Shahrashoob, A. (2025). AI-driven control algorithm using machine learning and genetic optimization for enhancing visual comfort in adaptive façades. Automation in Construction, 179, Article 106474. https://doi.org/10.1016/j.autcon.2025.106474.
Abstract
Effective management of daylight and visual comfort in office spaces remains a challenge, as existing shading systems often lack adaptability to changing environmental conditions and occupant needs. This paper presents an AI-driven real-time shading control algorithm that optimizes visual comfort using machine learning-based surrogate models and evolutionary optimization. A non-conventional adaptive façade was simulated using Radiance and Ladybug Tools across nine U.S. climates. Four machine learning models were evaluated for predicting Task Illuminance (Et) and Vertical Eye Illuminance (Ev), with Extra Trees achieving the highest accuracy (R2
= 0.95). A Non-dominated Sorting Genetic Algorithm II (NSGA-II) balances glare reduction and daylight utilization by optimizing façade configurations in real time. In contrast to prior approaches constrained to fixed geometries and single-objective control, this paper introduces a generalizable multi-objective control framework. Results show that AI-driven optimization significantly improves adaptive façade performance, offering a scalable solution for intelligent daylight and comfort management.
Keywords
Smart façade control; Machine learning; Surrogate models; Visual comfort; Task illuminance; Vertical eye illuminance; Dynamic shading