The intensive care unit (ICU) plays a crucial role in modern hospitals. ICU patients endure severe physical and mental conditions, making it essential to create a healing environment that reduces stress and promotes recovery. Among common environmental parameters, lighting conditions are particularly critical, as patients often face challenges with mobility and body positioning. Kinetic facades with adjustable external shading elements have gained attention for their ability to regulate sunlight effectively. However, their complexity poses challenges for design and implementation. This study proposes a reinforcement learning-based method, using Q-learning to handle discrete facade configurations and adaptive control under varying solar conditions for optimizing facade configurations in ICU rooms. The method aims to: (1) reduce direct sunlight glare and heat; and (2) maximize landscape views. A case study at Providence Alaska Medical Center demonstrates the method’s effectiveness, showing reduced glare and heat gain and improved landscape view availability through simulation. The results highlight the potential of reinforcement learning to address ICU-specific environmental challenges.
Dai et al. (Thu,) studied this question.