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Wind energy has an important part to play as renewable energy resource in a sustainable world. For a reliable integration of wind energy the volatile nature of wind has to be understood. This article shows how kernel methods and neural networks can serve as modeling, forecasting and monitoring techniques, and, how they contribute to a successful integration of wind into smart energy grids. First, we will employ kernel density estimation for modeling of wind data. Kernel density estimation allows a statistically sound modeling of time series data. The corresponding experiments are based on real data of wind energy time series from the NREL western wind resource dataset. Second, we will show how prediction of wind energy can be accomplished with the help of support vector regression. Last, we will use self-organizing feature maps to map high-dimensional wind time series to colored sequences that can be used for error detection.