Development of Load Analysis System using Customer Load Profile Data

• Published in: 2006 SICE-ICASE International Joint Conference pp. 1557 - 1561
• Main Authors: In Hyeob Yu, Il Kwon Yang, Jong Min Ko, Sun Ic Kim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2006
• Subjects: Automatic meter reading; Business; Clustering algorithms; Contracts; Customer Classification; Demand Pattern; Electricity supply industry; Electronic mail; Information analysis; Load forecasting; Load Profile; Pattern analysis; Sun
• ISBN: 9788995003848; 8995003847
• DOI: 10.1109/SICE.2006.315445

The multiple participants of the electricity market need new business strategies for surviving in competitive environments. Thus they need the accurate customer information of the electricity demand for providing value added services to customer. Demand characteristic is the most important one for analyzing customer information. In this study the load profile data, which can be collected through the automatic meter reading system, are analyzed for getting demand patterns of customer. The load profile data include electricity demand in 15 minutes interval. An algorithm for clustering similar demand patterns is developed using the load profile data. As results of classification, customers are separated into several groups. And the representative curves for the groups are generated. The number of groups is automatically generated. And it depends on the threshold value for the distance to separate groups. Also a demand analysis system is developed using the properties of the classified groups. Many functions of the system are described in this paper. It is expected that the demand characteristics of the system will be used for tariff design, load forecasting and load management. Also it will be a good infrastructure for making value added services related to electricity industry