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A novel reinforcement learning method for improving occupant comfort via window opening and closing
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
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
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(English)Manuscript (preprint) (Other academic)
National Category
Building Technologies
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-30942OAI: oai:DiVA.org:du-30942DiVA, id: diva2:1360325
Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-10-11Bibliographically approved
In thesis
1. The reinforcement learning method: A feasible and sustainable control strategy for efficient occupant-centred building operation in smart cities
Open this publication in new window or tab >>The reinforcement learning method: A feasible and sustainable control strategy for efficient occupant-centred building operation in smart cities
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Over half of the world’s population lives in urban areas, a trend which is expected to only grow as we move further into the future. With this increasing trend in urbanisation, challenges are presented in the form of the management of urban infrastructure systems. As an essential infrastructure of any city, the energy system presents itself as one of the biggest challenges. As cities expand in population and economically, global energy consumption increases and as a result so do greenhouse gas (GHG) emissions. To achieve the 2030 Agenda’s sustainable development goal on energy (SDG 7), renewable energy and energy efficiency have been shown as key strategies for attaining SDG 7. As the largest contributor to climate change, the building sector is responsible for more than half of the global final energy consumption and GHG emissions. As people spend most of their time indoors, the demand for energy is made worse as a result of maintaining the comfort level of the indoor environment. However, the emergence of the smart city and the internet of things (IoT) offers the opportunity for the smart management of buildings. Focusing on the latter strategy towards attaining SDG 7, intelligent building control offers significant potential for saving energy while respecting occupant comfort (OC). Most intelligent control strategies, however, rely on complex mathematical models which require a great deal of expertise to construct thereby costing in time and money. Furthermore, if these are inaccurate then energy is wasted and the comfort of occupants is decreased. Moreover, any change in the physical environment such as retrofits result in obsolete models which must be re-identified to match the new state of the environment. This model-based approach seems unsustainable and so a new model-free alternative is proposed. One such alternative is the reinforcement learning (RL) method. This method provides a beautiful solution to accomplishing the tradeoff between energy efficiency and OC within the smart city and more importantly to achieving SDG 7. To address the feasibility of RL as a sustainable control strategy for efficient occupant-centred building operation, a comprehensive review of RL for controlling OC in buildings as well as a case study implementing RL for improving OC via a window system are presented. The outcomes of each seem to suggest RL as a feasible solution, however, more work is required in the form of addressing current open issues such as cooperative multi-agent RL (MARL) needed for multi-occupant/multi-zonal buildings.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2019
Series
Dalarna Licentiate Theses in Microdata Analysis ; 11
Keywords
Markov decision processes, Reinforcement learning, Control, Building, Indoor comfort, Occupant
National Category
Building Technologies
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30613 (URN)978-91-88679-03-1 (ISBN)
Presentation
2019-11-01, B310, Borlänge, 10:00 (English)
Opponent
Supervisors
Available from: 2019-10-11 Created: 2019-10-07 Last updated: 2019-10-14Bibliographically approved

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Han, MengjieMay, RossZhang, Xingxing

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CiteExportLink to record
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