Name
Title | ||
|---|---|---|
An Event-Driven Convolutional Neural Architecture for Non-Intrusive Load Monitoring of Residential Appliance. |
Abstract | ||
|---|---|---|
Nowadays, the advancement of non-intrusive load monitoring (NILM) is hastened by the everincreasing requirements for smart power utilization and demand side management. Thus, an intelligent event-driven non-intrusive load monitoring method based on convolutional neural network (CNN) is proposed in this article to profile residential consumer behavior, which focused on limited applicability and low... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCE.2020.2977964 | IEEE Transactions on Consumer Electronics |
Keywords | DocType | Volume |
Monitoring,Feature extraction,Home appliances,Convolutional neural networks,Machine learning,Load modeling | Journal | 66 |
Issue | ISSN | Citations |
2 | 0098-3063 | 5 |
PageRank | References | Authors |
0.44 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongsheng Yang | 1 | 9 | 1.53 |
| Xiaoting Gao | 2 | 6 | 0.80 |
| Liang Kong | 3 | 5 | 0.44 |
| Yongheng Pang | 4 | 17 | 1.67 |
| Zhou Bowen | 5 | 18 | 2.69 |