AI-Driven Adaptive Façades for Daylight and Visual Comfort Optimization
Effective daylight management and visual comfort in office spaces remain crucial for occupant well-being and productivity. Traditional shading systems often fail to adapt to dynamic environmental conditions and individual preferences, leading to discomfort and energy inefficiency. This research explores AI-driven adaptive façades as a solution, integrating real-time control algorithms and predictive modeling to enhance indoor lighting quality and optimize energy usage.
Problem Statement
Current shading solutions in office environments are typically limited to static or manually adjustable systems. These fixed geometries cannot respond dynamically to variations in sunlight, glare, or occupant requirements. The lack of adaptability restricts optimal daylight utilization and visual comfort, highlighting the need for intelligent, real-time façade control systems that can respond to changing conditions.
Methodology
The study implements a real-time shading control algorithm that combines machine learning-based surrogate models with evolutionary optimization techniques. The adaptive façade was simulated across nine diverse U.S. climates using Radiance and Ladybug Tools. Four machine learning models were trained to predict Task Illuminance and Vertical Eye Illuminance, with the Extra Trees model achieving the highest accuracy (R² = 0.95).
AI Optimization Framework
A Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed to balance glare reduction and daylight utilization by optimizing façade configurations in real time. Unlike previous approaches, this multi-objective framework allows simultaneous optimization of multiple comfort parameters, providing a more flexible and generalizable solution for diverse building conditions.
Results and Discussion
Simulation results demonstrate that AI-driven façade control significantly enhances visual comfort and daylight management compared to conventional fixed-geometry approaches. The system adapts to changing climatic conditions and occupant needs, providing a scalable solution for energy-efficient, occupant-centered building design.
Conclusion and Future Work
This study highlights the potential of AI-enabled adaptive façades to transform office lighting and comfort strategies. Future work could extend to integrating occupant behavior analytics, real-time weather forecasts, and other environmental sensors to further refine predictive models and control strategies, pushing the boundaries of intelligent, sustainable architecture.
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