November 1, 2018 Journal article Open Access

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

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{
  "description": "<p>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&#39;s HVAC system. Apart from demonstrating a conflicting relationship, they depend not only on the building&#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&#39; thermal comfort and limiting energy consumption. In contrast to to existing methods, this approach focuses on a plug&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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "School of ECE, National Technical University of Athens, Greece", 
      "@type": "Person", 
      "name": "Marantos, Charalampos"
    }, 
    {
      "affiliation": "School of ECE, National Technical University of Athens, Greece", 
      "@type": "Person", 
      "name": "Lamparkos, Christos"
    }, 
    {
      "affiliation": "School of ECE, National Technical University of Athens, Greece", 
      "@type": "Person", 
      "name": "Tsoutsouras, Vasileios"
    }, 
    {
      "affiliation": "Department of Physics, Aristotle University of Thessaloniki, Greece", 
      "@type": "Person", 
      "name": "Siozios, Kostas"
    }, 
    {
      "affiliation": "School of ECE, National Technical University of Athens, Greece", 
      "@type": "Person", 
      "name": "Soudris, Dimitrios"
    }
  ], 
  "headline": "Towards plug&play smart thermostats inspired by reinforcement learning", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-11-01", 
  "url": "https://zenodo.org/record/3380093", 
  "keywords": [
    "HVAC control", 
    "Intelligent agents", 
    "Energy efficiency", 
    "Learning systems", 
    "Decision making", 
    "Embedded software"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1145/3285017.3285024", 
  "@id": "https://doi.org/10.1145/3285017.3285024", 
  "@type": "ScholarlyArticle", 
  "name": "Towards plug&play smart thermostats inspired by reinforcement learning"
}