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A novel reinforcement learning method for improving occupant comfort via window opening and closing
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.ORCID-id: 0000-0003-4212-8582
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.ORCID-id: 0000-0002-0551-9341
Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.ORCID-id: 0000-0002-2369-0169
Visa övriga samt affilieringar
(Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
Nationell ämneskategori
Husbyggnad
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-30942OAI: oai:DiVA.org:du-30942DiVA, id: diva2:1360325
Tillgänglig från: 2019-10-11 Skapad: 2019-10-11 Senast uppdaterad: 2019-10-11Bibliografiskt granskad
Ingår i avhandling
1. The reinforcement learning method: A feasible and sustainable control strategy for efficient occupant-centred building operation in smart cities
Öppna denna publikation i ny flik eller fönster >>The reinforcement learning method: A feasible and sustainable control strategy for efficient occupant-centred building operation in smart cities
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2019
Serie
Dalarna Licentiate Theses ; 12
Nyckelord
Markov decision processes, Reinforcement learning, Control, Building, Indoor comfort, Occupant
Nationell ämneskategori
Husbyggnad
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-30613 (URN)978-91-88679-03-1 (ISBN)
Presentation
2019-11-01, B310, Borlänge, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-10-11 Skapad: 2019-10-07 Senast uppdaterad: 2020-01-10Bibliografiskt granskad

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Han, MengjieMay, RossZhang, Xingxing

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Totalt: 261 träffar
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