Abstract
Today, load characteristic analysis plays a vital role in network planning, operation and control. In particular, with massive demand side participation activities on the network, the characteristics of individual load determines the way of the active load management as well as the network pricing strategies. In this paper, the load characteristics are analyzed utilizing clustering techniques for a tropical isle with massive temperature sensitive loads. Through deeply mining of real data, the features of individual loads were observed to define the way of load participation.
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