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Non-Intrusive Load Monitoring Based on Deep Pairwise-Supervised Hashing to Detect Unidentified Appliances
| Content Provider | MDPI |
|---|---|
| Author | Zhao, Qiang Xu, Yao Wei, Zhenfan Han, Yinghua |
| Copyright Year | 2021 |
| Description | Non-intrusive load monitoring (NILM) is a fast developing technique for appliances operation recognition in power system monitoring. At present, most NILM algorithms rely on the assumption that all fluctuations in the data stream are triggered by identified appliances. Therefore, NILM of identifying unidentified appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary unidentified appliances, we propose a voltage-current (V-I) trajectory enabled deep pairwise-supervised hashing (DPSH) method for NILM. DPSH performs simultaneous feature learning and hash-code learning with deep neural networks, which shows higher identification accuracy than a benchmark method. DPSH can generate different hash codes to distinguish identified appliances. For unidentified appliances, it generates completely new codes that are different from codes of multiple identified appliances to distinguish them. Experiments on public datasets show that our method can get better |
| Starting Page | 505 |
| e-ISSN | 22279717 |
| DOI | 10.3390/pr9030505 |
| Journal | Processes |
| Issue Number | 3 |
| Volume Number | 9 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-11 |
| Access Restriction | Open |
| Subject Keyword | Processes Industrial Engineering Non-intrusive Load Monitoring V-i Trajectory Deep Pairwise-supervised Hashing Feature Learning Hash-code Learning |
| Content Type | Text |
| Resource Type | Article |
