Journal article Open Access

Towards plug&play smart thermostats inspired by reinforcement learning

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


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    <dct:title>Towards plug&amp;play smart thermostats inspired by reinforcement learning</dct:title>
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    <dcat:keyword>HVAC control</dcat:keyword>
    <dcat:keyword>Intelligent agents</dcat:keyword>
    <dcat:keyword>Energy efficiency</dcat:keyword>
    <dcat:keyword>Learning systems</dcat:keyword>
    <dcat:keyword>Decision making</dcat:keyword>
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    <dct:description>&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;</dct:description>
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