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Article

A Reinforcement Learning-Based Approach to Automate the Electrochromic Glass and to Enhance the Visual Comfort

Faculty of Civil Engineering, University of Kaiserslautern, Paul-Ehrlich-Str. 14, 67663 Kaiserslautern, Germany
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Academic Editors: Barbara Strug and Grażyna Ślusarczyk
Appl. Sci. 2021, 11(15), 6949; https://doi.org/10.3390/app11156949
Received: 17 June 2021 / Revised: 21 July 2021 / Accepted: 23 July 2021 / Published: 28 July 2021
(This article belongs to the Special Issue Intelligent Computing in Architecture, Engineering and Construction)
Daylight is important for the well-being of humans. Therefore, many office buildings use large windows and glass facades to let more daylight into office spaces. However, this increases the chance of glare in office spaces, which results in visual discomfort. Shading systems in buildings can prevent glare but are not effectively adapted to changing sky conditions and sun position, thus losing valuable daylight. Moreover, many shading systems are also aesthetically unappealing. Electrochromic (EC) glass in this regard might be a better alternative, due to its light transmission properties that can be altered when a voltage is applied. EC glass facilitates zoning and also supports control of each zone separately. This allows the right amount of daylight at any time of the day. However, an effective control strategy is still required to efficiently control EC glass. Reinforcement learning (RL) is a promising control strategy that can learn from rewards and penalties and use this feedback to adapt to user inputs. We trained a Deep Q learning (DQN) agent on a set of weather data and visual comfort data, where the agent tries to adapt to the occupant’s feedback while observing the sun position and radiation at given intervals. The trained DQN agent can avoid bright daylight and glare scenarios in 97% of the cases and increases the amount of useful daylight up to 90%, thus significantly reducing the need for artificial lighting. View Full-Text
Keywords: daylighting; DQN; reinforcement learning; glare; electrochromic glass; radiance; visual comfort; simulations daylighting; DQN; reinforcement learning; glare; electrochromic glass; radiance; visual comfort; simulations
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MDPI and ACS Style

Kalyanam, R.; Hoffmann, S. A Reinforcement Learning-Based Approach to Automate the Electrochromic Glass and to Enhance the Visual Comfort. Appl. Sci. 2021, 11, 6949. https://doi.org/10.3390/app11156949

AMA Style

Kalyanam R, Hoffmann S. A Reinforcement Learning-Based Approach to Automate the Electrochromic Glass and to Enhance the Visual Comfort. Applied Sciences. 2021; 11(15):6949. https://doi.org/10.3390/app11156949

Chicago/Turabian Style

Kalyanam, Raghuram, and Sabine Hoffmann. 2021. "A Reinforcement Learning-Based Approach to Automate the Electrochromic Glass and to Enhance the Visual Comfort" Applied Sciences 11, no. 15: 6949. https://doi.org/10.3390/app11156949

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