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

  • 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.

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Metadaten
Author:Raghuram KalyanamORCiD, Sabine HoffmannORCiD
URN:urn:nbn:de:hbz:386-kluedo-66725
ISSN:2076-3417
Parent Title (English):Applied sciences
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language of publication:English
Publication Date:2021/07/28
Year of Publication:2021
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2021/12/07
Issue:2021, 11, 6949
Number of page:23
Source:10.3390/app11156949
Faculties / Organisational entities:Fachbereich Bauingenieurwesen
DDC-Cassification:6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Collections:Open-Access-Publikationsfonds
Licence (German):Zweitveröffentlichung