du.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A review of reinforcement learning methodologies on control systems for building energy
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.ORCID-id: 0000-0003-4212-8582
Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.ORCID-id: 0000-0002-2369-0169
Visa övriga samt affilieringar
2018 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Borlänge: Högskolan Dalarna, 2018. , s. 26
Serie
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2018:02
Nyckelord [en]
Reinforcement learning; Markov decision processes; building energy; control; multi-agent system
Nationell ämneskategori
Reglerteknik
Forskningsämne
Komplexa system - mikrodataanalys, Allmänt Mikrodataaanalys - metod
Identifikatorer
URN: urn:nbn:se:du-27956OAI: oai:DiVA.org:du-27956DiVA, id: diva2:1221058
Tillgänglig från: 2018-06-19 Skapad: 2018-06-19 Senast uppdaterad: 2018-06-20Bibliografiskt granskad

Open Access i DiVA

fulltext(1395 kB)220 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 1395 kBChecksumma SHA-512
fbe1506067884294b20da0f1b3a6e33790e15e9cf56c07eddc3336951f31c1b73026c7f1c2d900f6b5764efcae919e1e9b3edd45db910150e18f1bf549c07be3
Typ fulltextMimetyp application/pdf

Personposter BETA

Han, MengjieZhang, Xingxing

Sök vidare i DiVA

Av författaren/redaktören
Han, MengjieZhang, Xingxing
Av organisationen
MikrodataanalysEnergiteknik
Reglerteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 220 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 1074 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf