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LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning

机译：LightLearn：基于强化学习的自适应和居中照明控制器

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摘要

In commercial buildings, lighting contributes to about 20% of the total energy consumption. Lighting controllers that integrate occupancy and luminosity sensors to improve energy efficiency have been proposed. However, they are often ineffective because they focus solely on energy consumption rather than providing comfort to the occupants. An ideal controller should adapt itself to the preferences of the occupant and the environmental conditions. In this article, we introduce LightLearn, an occupant centered controller (OCC) for lighting based on Reinforcement Learning (RL). We describe the theory and hardware implementation of LightLearn. Our experiment during eight weeks in five offices shows that LightLearn learns the individual occupant behaviors and indoor environmental conditions, and adapts its control parameters accordingly by determining personalized set-points. Participants reported that the overall lighting was slightly improved compared to prior lighting conditions. We compare LightLearn to schedule-based and occupancy-based control scenarios, and evaluate their performance with respect to total energy use, light-utilization-ratio, unmet comfort hours, as well as light-comfort-ratio, which we introduce in this paper. We show that only LightLearn balances successfully occupant comfort and energy consumption. The adaptive nature of LightLearn suggests that reinforcement learning based occupant centered control is a viable approach to mitigate the discrepancy between occupant comfort and the goals of building control.
机译：在商业建筑中，照明约占总能耗的20％。已经提出了将占用传感器和亮度传感器集成以提高能量效率的照明控制器。然而，它们通常是无效的，因为它们仅关注能量消耗而不是为乘员提供舒适感。理想的控制器应适应乘员的喜好和环境条件。在本文中，我们介绍LightLearn，这是一种基于强化学习（RL）的照明用乘员中央控制器（OCC）。我们描述了LightLearn的理论和硬件实现。我们在五个办公室进行的为期八周的实验表明，LightLearn可以学习每个乘员的行为和室内环境条件，并通过确定个性化的设定点来相应地调整其控制参数。参与者报告说，与以前的照明条件相比，整体照明略有改善。我们将LightLearn与基于计划表和基于占用率的控制方案进行比较，并评估它们在总能耗，光利用率，未满足的舒适时间以及光舒适率方面的性能。 。我们证明只有LightLearn才能成功平衡乘员的舒适度和能耗。 LightLearn的自适应特性表明，基于强化学习的以乘员为中心的控制是减轻乘员舒适度与建筑物控制目标之间差异的可行方法。

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