Kramer, OliverTreiber, Nils AndréGieseke, FabianPage, BerndFleischer, Andreas G.Göbel, JohannesWohlgemuth, Volker2019-09-162019-09-162013https://dl.gi.de/handle/20.500.12116/25850In recent years, there has been a significant increase in energy produced by sustainable resources like wind- and solar power plants. This led to a shift from traditional energy systems to so-called smart grids (i.e., distributed systems of energy suppliers and consumers). While the sustainable energy resources are very appealing from an environmental point of view, their volatileness renders the integration into the overall energy system difficult. For this reason, shortterm wind and solar energy prediction systems are essential for balance authorities to schedule spinning reserves and reserve energy. In this chapter, we build upon our previous work and provide a detailed practical analysis of several wind energy learning scenarios. Our approach makes use of support vector regression models, one of the state-of-the art techniques in the field of machine learning, to build effective predictors for single wind turbines based on data given for neighbored turbines.Text/Conference Paper