Name
Abstract | ||
|---|---|---|
The largest source of energy consumption in buildings is heating, ventilation, and air conditioning (HVAC). For an HVAC system to provide comfort and minimize energy consumption, it is crucial to understand the spatiotemporal thermal dynamics, especially in large open spaces. To optimize HVAC control, it is important to establish accurate dynamic thermal models. For this purpose, we constructed a real-world test bed by instrumenting an HVAC-controller auditorium using multiple types of sensors. Based on the dataset, we develop and evaluate a novel data-driven approach to model the complex thermal dynamics in a large space through a combination of data clustering and system identification techniques. Real-world data shows that our approach achieves low estimation errors. Our modeling approach therefore provides a practical foundation for HVAC control and optimization for large open spaces. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDCS.2014.16 | ICDCS |
Keywords | DocType | ISSN |
hvac,cyber physical systems,hvac controlled real-life auditorium,spatiotemporal thermal dynamics,dynamic thermal modeling,heating,building,cyber physical systems, sensor networks, modeling, hvac,data-driven approach,system identification technique,sensor networks,energy consumption,data clustering,ventilation,modeling,air conditioning,building management systems | Conference | 1063-6927 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Fu | 1 | 97 | 5.23 |
| Mo Sha | 2 | 251 | 23.52 |
| Chengjie Wu | 3 | 208 | 9.22 |
| Andrew Kutta | 4 | 0 | 0.34 |
| Anna Leavey | 5 | 0 | 0.34 |
| Chenyang Lu | 6 | 6474 | 385.38 |
| Humberto Gonzalez | 7 | 0 | 0.34 |
| Weining Wang | 8 | 111 | 11.74 |
| Bill Drake | 9 | 0 | 0.34 |
| Yixin Chen | 10 | 0 | 0.34 |
| Pratim Biswas | 11 | 1 | 0.69 |