Dynamic Data Driven Dimensioning of Balancing Power with k-Nearest Neighbors
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Date
2015-01-14
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Abstract
This paper proposes a novel dynamic design for
control reserve dimensioning. In contrast to the current statistical
analytic design we present a data driven approach with methods
of computational intelligence. The chosen k-nearest neighbor
algorithm is one of the most sucessfully used methods in machine
learning. The model is able to predict complex nonlinear behavior
by assuming that similar observations have similar outcomes.
A condition for the success of this method is to determine
the salient features. Therefore the core of this paper is to
show the dependencies of the influencing parameters. Numerical
experiments on the basis of freely available data for the years
2011 until 2013 show that there are time and space patterns as
well as inter dependencies with the active power market.
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Keywords
Control Power, k-Nearest Neighbor