Name
Abstract | ||
|---|---|---|
In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21186052 | SENSORS |
Keywords | DocType | Volume |
smart home, scaled model, machine learning, temperature prediction | Journal | 21 |
Issue | ISSN | Citations |
18 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Burns | 1 | 0 | 0.34 |
| Gregory Fichthorn | 2 | 0 | 0.34 |
| Jason Ling | 3 | 0 | 0.34 |
| Sharare Zehtabian | 4 | 1 | 1.72 |
| Salih S Bacanlı | 5 | 0 | 0.34 |
| Ladislau Bölöni | 6 | 335 | 42.82 |
| Damla Turgut | 7 | 1127 | 87.39 |