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Towards plug&play smart thermostats inspired by reinforcement learning

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


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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="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <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>
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