Name
Abstract | ||
|---|---|---|
Electricity grids around the world are undergoing a fundamental transformation, thanks to the modernization of electricity distribution systems including smart meter deployment and applications. Data generated by smart meters provides a wealth of information that can help better understand and optimize the operation of electricity networks. This paper proposes a novel Hybrid Load Profile Clustering (HLPC) algorithm to identify patterns of electricity consumption of users from large volumes of residential electricity consumption interval data. The HLPC algorithm is in particular advantageous in detecting 'spike' patterns of consumption among other various residential consumption patterns. Experimental results about the HLPC algorithm are presented using interval data from 600 smart meters in Victoria, Australia, to demonstrate that the proposed methodology outperforms standard clustering algorithms. |
Year | Venue | Field |
|---|---|---|
2016 | PROCEEDINGS 2016 IEEE 25TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE) | Data mining,Software deployment,Metre,Electricity,Electric power distribution,Load profile,Feature extraction,Engineering,Smart meter,Cluster analysis |
DocType | ISSN | Citations |
Conference | 2163-5137 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangxia Zhong | 1 | 3 | 1.77 |
| Jia Wang | 2 | 79 | 17.75 |
| Xinghuo Yu | 3 | 3954 | 300.63 |
| Qingmai Wang | 4 | 37 | 4.40 |
| Miguel E. Combariza | 5 | 0 | 0.34 |
| Grahame Holmes | 6 | 29 | 4.32 |