File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/PESGM.2016.7741464
- Scopus: eid_2-s2.0-85001908073
- WOS: WOS:000399937901108
- Find via
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: Residential smart meter data compression and pattern extraction via non-negative K-SVD
Title | Residential smart meter data compression and pattern extraction via non-negative K-SVD |
---|---|
Authors | |
Keywords | Big data Data compression K-SVD Pattern extraction Residential consumer Smart meter |
Issue Date | 2016 |
Citation | 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, 17-21 July 2016. In Conference Proceedings, 2016 How to Cite? |
Abstract | Smart meter plays a vital role in the management of smart grid. Massive electricity consumption data are being collected with the popularity of smart meters, which pushes the electricity demand side into a big data world and poses great challenges to data communication and storage. The residential electricity consumption behaviors vary with different lifestyles and family configurations. It's assumed that each daily load profile is essentially a combination of several certain hidden usage patterns. On this basis, a K-SVD based data compression technique is proposed in this paper to decompose each overall load profile into a linear combination of several meaningful partial patterns and minimize the reconstruction error using sparse and redundant representations. Comparisons with discrete Wavelet transform (DWT) and principal component analysis (PCA) are conducted. The results show that the proposed method can achieve better compression quality and identify meaningful hidden usage patterns. |
Persistent Identifier | http://hdl.handle.net/10722/308709 |
ISSN | 2020 SCImago Journal Rankings: 0.345 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Yi | - |
dc.contributor.author | Chen, Qixin | - |
dc.contributor.author | Kang, Chongqing | - |
dc.contributor.author | Xia, Qing | - |
dc.contributor.author | Tan, Yuekai | - |
dc.contributor.author | Zeng, Zhijian | - |
dc.contributor.author | Luo, Min | - |
dc.date.accessioned | 2021-12-08T07:49:58Z | - |
dc.date.available | 2021-12-08T07:49:58Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, 17-21 July 2016. In Conference Proceedings, 2016 | - |
dc.identifier.issn | 1944-9925 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308709 | - |
dc.description.abstract | Smart meter plays a vital role in the management of smart grid. Massive electricity consumption data are being collected with the popularity of smart meters, which pushes the electricity demand side into a big data world and poses great challenges to data communication and storage. The residential electricity consumption behaviors vary with different lifestyles and family configurations. It's assumed that each daily load profile is essentially a combination of several certain hidden usage patterns. On this basis, a K-SVD based data compression technique is proposed in this paper to decompose each overall load profile into a linear combination of several meaningful partial patterns and minimize the reconstruction error using sparse and redundant representations. Comparisons with discrete Wavelet transform (DWT) and principal component analysis (PCA) are conducted. The results show that the proposed method can achieve better compression quality and identify meaningful hidden usage patterns. | - |
dc.language | eng | - |
dc.relation.ispartof | 2016 IEEE Power and Energy Society General Meeting (PESGM) | - |
dc.subject | Big data | - |
dc.subject | Data compression | - |
dc.subject | K-SVD | - |
dc.subject | Pattern extraction | - |
dc.subject | Residential consumer | - |
dc.subject | Smart meter | - |
dc.title | Residential smart meter data compression and pattern extraction via non-negative K-SVD | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/PESGM.2016.7741464 | - |
dc.identifier.scopus | eid_2-s2.0-85001908073 | - |
dc.identifier.eissn | 1944-9933 | - |
dc.identifier.isi | WOS:000399937901108 | - |