Name
Abstract | ||
|---|---|---|
Energy disaggregation helps to identify major energy guzzlers in the house without introducing extra metering cost. It motivates users to take proper actions for energy saving and facilitates demand response programs. To increase the accuracy of energy disaggregation and reduce the computational complexity, we use the occupancy information and split the whole time interval into occupied and unoccupied periods. In unoccupied periods, we apply simple energy approximation; in occupied periods, we perform energy disaggregation with existing methods. Real-world implementation and evaluation are conducted in an apartment. Comparing with energy disaggregation without considering occupancy information, our occupancy-aided approach can significantly reduce the computational overhead while ensuring the accuracy of energy disaggregation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2939912.2939914 | e-Energy (Posters) |
Field | DocType | Citations |
Overhead (computing),Simulation,Demand response,Occupancy,Engineering,Metering mode,Computational complexity theory,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoming Tang | 1 | 67 | 17.62 |
| Kui Wu | 2 | 32 | 6.79 |