Name
Abstract | ||
|---|---|---|
In power systems, load curve data is one of the most important datasets that are collected and retained by utilities. The quality of load curve data, however, is hard to guarantee since the data is subject to communication losses, meter malfunctions, and many other impacts. In this paper, a new approach to analyzing load curve data is presented. The method adopts a new view, termed portrait, on the load curve data by analyzing the periodic patterns in the data and reorganizing the data for ease of analysis. Furthermore, we introduce algorithms to build the virtual portrait load curve data, and demonstrate its application on load curve data cleansing. Compared to existing regression-based methods, our method is much faster and more accurate for both small-scale and large-scale real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TSG.2014.2311415 | IEEE TRANSACTIONS ON SMART GRID |
Keywords | Field | DocType |
Load curve data cleansing, pattern analysis | Data mining,Anomaly detection,Data cleansing,Regression,Computer science,Pattern analysis,Electric power system,Metre (music),Periodic graph (geometry) | Journal |
Volume | Issue | ISSN |
5 | 4 | 1949-3053 |
Citations | PageRank | References |
3 | 0.44 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoming Tang | 1 | 67 | 17.62 |
| Kui Wu | 2 | 32 | 6.79 |
| Jingsheng Lei | 3 | 691 | 69.87 |
| Zhongqin Bi | 4 | 5 | 2.50 |
| Jiuyang Tang | 5 | 46 | 12.86 |