Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing
Abstract With the development of smart grids, appliance‐level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non‐intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applicat...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-11-01
|
| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12242 |
| _version_ | 1811272571343077376 |
|---|---|
| author | Yinghua Han Yao Xu Yaxin Huo Qiang Zhao |
| author_facet | Yinghua Han Yao Xu Yaxin Huo Qiang Zhao |
| author_sort | Yinghua Han |
| collection | DOAJ |
| description | Abstract With the development of smart grids, appliance‐level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non‐intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage–current (V–I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V–I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large‐scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state‐of‐art methods. |
| first_indexed | 2024-04-12T22:42:42Z |
| format | Article |
| id | doaj.art-8995d098513c4ccf903ffb9ba5ea0a9e |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-12T22:42:42Z |
| publishDate | 2021-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-8995d098513c4ccf903ffb9ba5ea0a9e2022-12-22T03:13:39ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-11-0115213066308010.1049/gtd2.12242Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashingYinghua Han0Yao Xu1Yaxin Huo2Qiang Zhao3School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao ChinaSchool of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao ChinaSchool of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao ChinaCollege of Information Science and Engineering Northeastern University Shenyang ChinaAbstract With the development of smart grids, appliance‐level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non‐intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage–current (V–I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V–I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large‐scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state‐of‐art methods.https://doi.org/10.1049/gtd2.12242Other topics in statisticsOptimisation techniquesImage and video codingPower system management, operation and economicsDomestic appliancesOther topics in statistics |
| spellingShingle | Yinghua Han Yao Xu Yaxin Huo Qiang Zhao Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing IET Generation, Transmission & Distribution Other topics in statistics Optimisation techniques Image and video coding Power system management, operation and economics Domestic appliances Other topics in statistics |
| title | Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing |
| title_full | Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing |
| title_fullStr | Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing |
| title_full_unstemmed | Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing |
| title_short | Non‐intrusive load monitoring by voltage–current trajectory enabled asymmetric deep supervised hashing |
| title_sort | non intrusive load monitoring by voltage current trajectory enabled asymmetric deep supervised hashing |
| topic | Other topics in statistics Optimisation techniques Image and video coding Power system management, operation and economics Domestic appliances Other topics in statistics |
| url | https://doi.org/10.1049/gtd2.12242 |
| work_keys_str_mv | AT yinghuahan nonintrusiveloadmonitoringbyvoltagecurrenttrajectoryenabledasymmetricdeepsupervisedhashing AT yaoxu nonintrusiveloadmonitoringbyvoltagecurrenttrajectoryenabledasymmetricdeepsupervisedhashing AT yaxinhuo nonintrusiveloadmonitoringbyvoltagecurrenttrajectoryenabledasymmetricdeepsupervisedhashing AT qiangzhao nonintrusiveloadmonitoringbyvoltagecurrenttrajectoryenabledasymmetricdeepsupervisedhashing |