Abstract
In this paper, we study the role of analytics for electricity consumption in smart grids and their possible applications like detecting fraud. Using data-sets of industrial as well as residential consumers, we show how incomplete clustering can help to reduce the search space for these applications. We provide a framework for iterative incomplete clustering and illustrate results in our data-sets. We find, incomplete clustering via correlation coefficients can identify a variety of different households and industries with unique characteristics that are missed with other clustering approaches.
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