Dynamic Data Driven Dimensioning of Balancing Power with k-Nearest Neighbors

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.