Name
Abstract | ||
|---|---|---|
Occupant behavior and space occupancy provide important information to controlling and optimizing energy use in buildings, especially when it comes to heating/cooling, where Heating, Ventilation, and Air Conditioning (HVAC) systems are in use. Besides, thermal comfort is mainly occupant behavior-dependent, based on his movements and space occupancy inside a building over the daytime. Traditional HVAC system functioning, based on turning OFF/ON of the system at the building level, without taking into account space occupancy, can lead to unnecessary heating/cooling of some rooms, which results in a waste of energy, or an under-heating/undercooling of the rooms leading to a lack of comfort. To optimize energy consumption and occupant comfort, we introduce in this paper a temporal graph-based approach for occupants' behavior modeling for energy consumption optimization at room level. Our approach combines a graph learning algorithm, a hierarchical clustering to identify frequent occupants movements within the optimal time interval decomposition of days, and a multi-objective problem resolution. We experimented our approach on a 4-week dataset of 4 occupants movements among office rooms. The first results showed that our model helps minimize energy consumption by up to 62.21% compared to conventional functioning of HVAC systems, and fulfills up to 94.02% of occupants' thermal comfort. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IWCMC.2019.8766569 | 2019 15TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) |
Keywords | Field | DocType |
Smart building, Occupant behavior, Energy optimization, Comfort optimization, Graph mining | Air conditioning,Automotive engineering,Ventilation (architecture),Computer science,HVAC,Thermal comfort,Occupancy,Building automation,Energy consumption,Energy minimization,Distributed computing | Conference |
ISSN | Citations | PageRank |
2376-6492 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nour Haidar | 1 | 0 | 0.34 |
| Nouredine Tamani | 2 | 88 | 14.63 |
| Yacine Ghamri-Doudane | 3 | 759 | 83.02 |
| Alain Bouju | 4 | 0 | 0.34 |