Name
Abstract | ||
|---|---|---|
The Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to individual households' electricity usage data. The main task of this analysis is to identify a way of representing the variability of a households behavior and to develop an efficient way of clustering the households into a few, usable and homogenous groups. The regularity in terms of the electricity usage is useful information for organizations to allow accurate demand planning with the aim of improving the overall efficiency of the network. The approach is tested using data from 46 households located in Austin, Texas, USA and monitored for 14 months at a sampling interval of 1 hour. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/INISTA.2017.8001151 | 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) |
Keywords | Field | DocType |
grade correspondence analysis,households segmentation,electricity usage patterns,smart meter data | USable,Data mining,Informatics,Data visualization,Visualization,Segmentation,Engineering,Usage data,Cluster analysis,Correspondence analysis | Conference |
ISBN | Citations | PageRank |
978-1-5090-5796-2 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomasz Zabkowski | 1 | 32 | 11.28 |
| Krzysztof Gajowniczek | 2 | 19 | 6.14 |