Abstract

Smart grid technologies provide an effective utilization of customer load profiles. The load profiles show different daily patterns depending on different industrial and commercial classifications, which employ time-of-use (TOU) tariff structure in South Korea. For effective analysis of daily load profiles under the TOU tariff, this paper introduces a new parameter, TOU index, to characterize and classify the load profiles. Actual load profiles achieved from general and industrial customers are utilized. The characterization using the TOU index derives 4 groups of the load profiles relating to the TOU rate schedules. This paper also develops a classification method for the load profiles using K-means and self-organizing maps (SOM) clustering. The K-means clustering with 4-cluster and the SOM clustering with 5×1 dimension produces the most effective classification result. In addition, the SOM method with 5×1 dimension shows the relatively best performance.

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