An ideal lighting controller should balance between occupant comfort and energy efficiency. In a prior study, we introduced LightLearn, an occupant centered lighting controller based on reinforcement learning and demonstrated its control operation under real conditions in a university building. The controller learned individual occupant preferences and indoor environmental conditions, and adapted its control parameters accordingly by determining personalized set-points. In this paper, we implement a simulation framework to comprehensively evaluate LightLearn. This framework consists of Python and EnergyPlus, and we simulated the performance of LightLearn under various indoor lighting conditions and occupant behaviors. These changes vary the interactions between LightLearn and human input, which effects the performance of the control system. Despite such variations, we show how LightLearn adapts to any indoor light condition and occupant behavior, and determines the optimal policy for balancing occupant comfort and energy efficiency.
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