Reinforcement Learning-Enabled Adaptive Control for Climate-Responsive Kinetic Building Facades

As people spend most of their time indoors, the quality of the indoor lighting environment plays a crucial role in occupant health, mood, and productivity. While modern glazed curtain walls improve daylighting potential, they also heighten the risks of glare and associated solar heat gains that may result in occupant discomfort and overheating. To continuously ensure visual comfort while providing shading, kinetic responsive facades controlled by sensors and actuators can change the angles of the elements. Conventional control methods for shading devices mainly involve the unified control of each element. However, as each element of the kinetic responsive facade can be controlled independently, the number of potential control actions increases exponentially with the number of facade elements and possible angles. Traditional rule-based methods are challenging for handling this multi-objective high-dimensional control problem. This paper introduces a novel self-learning, real-time reinforcement learning (RL) controller that can interact with the environment to find a globally optimal control solution for each element in kinetic responsive facades, thereby meeting visual quality and shading goals. The configuration and workflow of the proposed RL controller are introduced and tested vertically, diagonally, and radially folding responsive facades. The results demonstrate that the proposed RL controller effectively maintains horizontal and vertical illuminance, with 72.92% of test points in occupied spaces falling within the defined comfort range. Additionally, it keeps the daylight glare probability (DGP) below 0.35, a level generally considered imperceptible.