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Reinforcement learning control for indoor comfort: A survey
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-0551-9341
Dalarna University, School of Technology and Business Studies, Energy Technology.ORCID iD: 0000-0002-2369-0169
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
2019 (English)In: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 609, no 6, article id 062011Conference paper, Published paper (Refereed)
Abstract [en]

Building control systems are prone to fail in complex and dynamic environments. The reinforcement learning (RL) method is becoming more and more attractive in automatic control. The success of the reinforcement learning method in many artificial intelligence applications has resulted in an open question on how to implement the method in building control systems. This paper therefore conducts a comprehensive review of the RL methods applied in control systems for indoor comfort and environment. The empirical applications of RL-based control systems are then presented, depending on optimisation objectives and the measurement of energy use. This paper illustrates the class of algorithms and implementation details regarding how the value functions have been represented and how the policies are improved. This paper is expected to clarify the feasible theory and functions of RL for building control systems, which would promote their wider-spread application and thus contribute to the social economic benefits in the energy and built environments.

Place, publisher, year, edition, pages
2019. Vol. 609, no 6, article id 062011
National Category
Civil Engineering Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-31167DOI: 10.1088/1757-899X/609/6/062011Scopus ID: 2-s2.0-85074523665OAI: oai:DiVA.org:du-31167DiVA, id: diva2:1375864
Conference
10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019; Bari; Italy; 5 September 2019 through 7 September 2019; Code 153083
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06

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May, RossZhang, XingxingHan, Mengjie

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
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  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
More languages
Output format
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