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dc.contributor.authorBarrett, Enda
dc.contributor.authorLinder, Stephen
dc.date.accessioned2017-12-01T16:14:53Z
dc.date.available2017-12-01T16:14:53Z
dc.date.issued2015-08-29
dc.identifier.citationBarrett, Enda, & Linder, Stephen. (2015). Autonomous HVAC Control, A Reinforcement Learning Approach. In Albert Bifet, Michael May, Bianca Zadrozny, Ricard Gavalda, Dino Pedreschi, Francesco Bonchi, Jaime Cardoso & Myra Spiliopoulou (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part III (pp. 3-19). Cham: Springer International Publishing.en_IE
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10379/6997
dc.description.abstractAbstract—Recent high profile developments of autonomous learning thermostats by companies such as Nest Labs and Honeywell have brought to the fore the possibility of ever greater numbers of intelligent devices permeating our homes and working environments into the future. However, the specific learning approaches and methodologies utilised by these devices have never been made public. In fact little information is known as to the specifics of how these devices operate and learn about their environments or the users who use them. This paper proposes a suitable learning architecture for such an intelligent thermostat in the hope that it will benefit further investigation by the research community. Our architecture comprises a number of different learning methods each of which contributes to create a complete autonomous thermostat capable of controlling a HVAC system. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. Our results show that the learning thermostat can achieve cost savings of 10% over a programmable thermostat, whilst maintaining high occupant comfort standards.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherSpringer Verlagen_IE
dc.relation.ispartofJoint European Conference on Machine Learning and Knowledge Discovery in Databasesen
dc.subjectHVAC controlen_IE
dc.subjectReinforcement learningen_IE
dc.subjectBayesian learningen_IE
dc.titleAutonomous hvac control, a reinforcement learning approachen_IE
dc.typeConference Paperen_IE
dc.date.updated2017-11-28T16:45:14Z
dc.identifier.doi10.1007/978-3-319-23461-8_1
dc.local.publishedsourcehttps://doi.org/10.1007/978-3-319-23461-8_1en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|
dc.internal.rssid13513798
dc.local.contactEnda Barrett, Room 427, It Department, Nui Galway. Email: enda.barrett@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionPUBLISHED
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