Name
Abstract | ||
|---|---|---|
Thanks to the large-scale smart meters deployments around the world, non-intrusive appliance load monitoring (NILM) is receiving popularity. It aims to disaggregate the total electricity load of a home into individual appliances without resorting to any specific appliance power monitors. NILM is worthy of broad attention owing to its facilitation in energy savings. This paper regards NILM as a classification task and proposes a two-step method based on graph signal processing (GSP). In the first step, a smoothest solution is obtained by minimizing the regularization term. In the second step, gradient projection method, which uses the obtained minimizer as a start point, is adopted to optimize the while objective function, where NILM is regarded as a constrained nonlinear programming problem. The experiment results based on the open-access data set REDD clearly demonstrate that the proposed GSP-based method achieves improved performance compared with other state-of-the-art low-rate NILM approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SPAC49953.2019.237866 | 2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) |
Keywords | DocType | ISBN |
Non-intrusive appliance load monitoring,graph signal processing,constrained nonlinear programming | Conference | 978-1-7281-5929-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing Zhang | 1 | 0 | 0.34 |
| Shengjie Zhao | 2 | 72 | 16.24 |
| Qingjiang Shi | 3 | 725 | 56.93 |
| Rongqing Zhang | 4 | 4 | 3.10 |