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Conference Paper
Segmentation of Electrical Customers Using Census Information and Power Load Patterns
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- Authors
- Heon Gyu Lee, Keun Ho Ryu, Yong Ho Shin
- Issue Date
- 2015-07
- Citation
- International Conference on Frontiers of Information Technology, Applications and Tools (FITAT) 2015, pp.148-153
- Language
- English
- Type
- Conference Paper
- Abstract
- For developing an accurate clustering model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by a dimension reduction conceptualized subspace clustering techniques suitable for highdimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load. The power demand pattern prediction accuracy was approximately 81%.
- KSP Keywords
- Cluster analysis(CA), Clustering Technique, Clustering model, Data cluster, Dimension Reduction, Domestic electricity demand, Electric power demand, Electricity demand forecasting, Electricity load, High-dimensional data, Load Monitoring