Journal article Open Access

# Towards plug&play smart thermostats inspired by reinforcement learning

Marantos, Charalampos; Lamparkos, Christos; Tsoutsouras, Vasileios; Siozios, Kostas; Soudris, Dimitrios

### DataCite XML Export

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<identifier identifierType="URL">https://zenodo.org/record/3380093</identifier>
<creators>
<creator>
<creatorName>Marantos, Charalampos</creatorName>
<givenName>Charalampos</givenName>
<familyName>Marantos</familyName>
<affiliation>School of ECE, National Technical University of Athens, Greece</affiliation>
</creator>
<creator>
<creatorName>Lamparkos, Christos</creatorName>
<givenName>Christos</givenName>
<familyName>Lamparkos</familyName>
<affiliation>School of ECE, National Technical University of Athens, Greece</affiliation>
</creator>
<creator>
<creatorName>Tsoutsouras, Vasileios</creatorName>
<givenName>Vasileios</givenName>
<familyName>Tsoutsouras</familyName>
<affiliation>School of ECE, National Technical University of Athens, Greece</affiliation>
</creator>
<creator>
<creatorName>Siozios, Kostas</creatorName>
<givenName>Kostas</givenName>
<familyName>Siozios</familyName>
<affiliation>Department of Physics, Aristotle University of Thessaloniki, Greece</affiliation>
</creator>
<creator>
<creatorName>Soudris, Dimitrios</creatorName>
<givenName>Dimitrios</givenName>
<familyName>Soudris</familyName>
<affiliation>School of ECE, National Technical University of Athens, Greece</affiliation>
</creator>
</creators>
<titles>
<title>Towards plug&amp;play smart thermostats inspired by reinforcement learning</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2018</publicationYear>
<subjects>
<subject>HVAC control</subject>
<subject>Intelligent agents</subject>
<subject>Energy efficiency</subject>
<subject>Learning systems</subject>
<subject>Decision making</subject>
<subject>Embedded software</subject>
</subjects>
<dates>
<date dateType="Issued">2018-11-01</date>
</dates>
<resourceType resourceTypeGeneral="JournalArticle"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3380093</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3285017.3285024</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Buildings are immensely energy-demanding and this fact is enhanced by the expectation of even more increment of energy consumption in the future. In order to mitigate this problem, a low-cost, flexible and high-quality Decision-Making Mechanism for supporting the tasks of a Smart Thermostat is proposed. Energy efficiency and thermal comfort are the two primary quantities regarding control performance of a building&amp;#39;s HVAC system. Apart from demonstrating a conflicting relationship, they depend not only on the building&amp;#39;s dynamics, but also on the surrounding climate and weather, thus rendering the problem of finding a long-term control scheme hard, and of stochastic nature. The introduced mechanism is inspired by Reinforcement Learning techniques and aims at satisfying both occupants&amp;#39; thermal comfort and limiting energy consumption. In contrast to to existing methods, this approach focuses on a plug&amp;amp;play solution, that does not require detailed building models and is applicable to a wide variety of buildings as it learns the dynamics using gathered information from the environment. The proposed control mechanisms were evaluated via a well-known building simulation framework and implemented on ARM-based, low-cost embedded devices.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/780572/">780572</awardNumber>
<awardTitle>Software Development toolKit for Energy optimization and technical Debt elimination</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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