Journal article Open Access

# Towards plug&play smart thermostats inspired by reinforcement learning

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

### Citation Style Language JSON Export

{
"DOI": "10.1145/3285017.3285024",
"author": [
{
"family": "Marantos, Charalampos"
},
{
"family": "Lamparkos, Christos"
},
{
"family": "Tsoutsouras, Vasileios"
},
{
"family": "Siozios, Kostas"
},
{
"family": "Soudris, Dimitrios"
}
],
"issued": {
"date-parts": [
[
2018,
11,
1
]
]
},
"abstract": "<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>",
"title": "Towards plug&play smart thermostats inspired by reinforcement learning",
"type": "article-journal",
"id": "3380093"
}
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