Repository logo
 
Publication

UCB1 Based Reinforcement Learning Model for Adaptive Energy Management in Buildings

2018Conference object Open access
Simple item page
dc.contributor.authorAndrade, Rui
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-18T10:27:39Z
dc.date.available2021-02-18T10:27:39Z
dc.date.issued2018
dc.description.abstractThis paper proposes a reinforcement learning model for intelligent energy management in buildings, using a UCB1 based approach. Energy management in buildings has become a critical task in recent years, due to the incentives to the increase of energy efficiency and renewable energy sources penetration. Managing the energy consumption, generation and storage in this domain, becomes, however, an arduous task, due to the large uncertainty of the different resources, adjacent to the dynamic characteristics of this environment. In this scope, reinforcement learning is a promising solution to provide adaptiveness to the energy management methods, by learning with the on-going changes in the environment. The model proposed in this paper aims at supporting decisions on the best actions to take in each moment, regarding buildings energy management. A UCB1 based algorithm is applied, and the results are compared to those of an EXP3 approach and a simple reinforcement learning algorithm. Results show that the proposed approach is able to achieve a higher quality of results, by reaching a higher rate of successful actions identification, when compared to the other considered reference approaches.pt_PT
dc.description.sponsorshipThis work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from Project SIMOCE (ANI|P2020 17690).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-319-99608-0_1pt_PT
dc.identifier.isbn978-3-319-99608-0
dc.identifier.urihttp://hdl.handle.net/10400.22/17032
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-319-99608-0_1pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectAdaptive learningpt_PT
dc.subjectEnergy management in buildingspt_PT
dc.subjectEXP3pt_PT
dc.subjectReinforcement learningpt_PT
dc.subjectUCB1pt_PT
dc.titleUCB1 Based Reinforcement Learning Model for Adaptive Energy Management in Buildingspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceToledo, Spainpt_PT
oaire.citation.endPage11pt_PT
oaire.citation.startPage3pt_PT
oaire.citation.titleDCAI 2018: Distributed Computing and Artificial Intelligence (DCAI 2018)pt_PT
oaire.citation.volume801pt_PT
person.familyNameAndrade
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.givenNameRui
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.identifier1408593
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier.ciencia-id751C-7ECE-59F0
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0003-2356-3706
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationa6a0a2b3-4d06-4fd2-8587-74a898a61679
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoverya6a0a2b3-4d06-4fd2-8587-74a898a61679

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
COM_GECAD_2018_ADRESS2018_Andrade.pdf
Size:
372.52 KB
Format:
Adobe Portable Document Format
Download

Collections

ISEP – GECAD – Comunicações em eventos científicos