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A review of reinforcement learning methodologies on control systems for building energy
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
Dalarna University, School of Technology and Business Studies, Energy Technology.ORCID iD: 0000-0002-2369-0169
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2018 (English)Report (Other academic)
Abstract [en]

The usage of energy directly leads to a great amount of consumption of the non-renewable fossil resources. Exploiting fossil resources energy can influence both climate and health via ineluctable emissions. Raising awareness, choosing alternative energy and developing energy efficient equipment contributes to reducing the demand for fossil resources energy, but the implementation of them usually takes a long time. Since building energy amounts to around one-third of global energy consumption, and systems in buildings, e.g. HVAC, can be intervened by individual building management, advanced and reliable control techniques for buildings are expected to have a substantial contribution to reducing global energy consumptions. Among those control techniques, the model-free, data-driven reinforcement learning method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has brought us an explicit indication of implementing the method on building energy control. Fruitful algorithms complement each other and guarantee the quality of the optimisation. As a central brain of smart building automation systems, the control technique directly affects the performance of buildings. However, the examination of previous works based on reinforcement learning methodologies are not available and, moreover, how the algorithms can be developed is still vague. Therefore, this paper briefly analyses the empirical applications from the methodology point of view and proposes the future research direction.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2018. , p. 26
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2018:02
Keywords [en]
Reinforcement learning; Markov decision processes; building energy; control; multi-agent system
National Category
Control Engineering
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
URN: urn:nbn:se:du-27956OAI: oai:DiVA.org:du-27956DiVA, id: diva2:1221058
Available from: 2018-06-19 Created: 2018-06-19 Last updated: 2018-06-20Bibliographically approved

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CiteExportLink to record
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