Abstract
Better prediction models for the upcoming supply of renewable energy are important to decrease the need of controlling energy provided by conventional power plants. Especially for successful power grid integration of the highly volatile wind power production, a reliable forecast is crucial. In this chapter, we focus on short-term wind power prediction and employ data from the National Renewable Energy Laboratory (NREL), which are designed for a wind integration study in the western part of the United States. In contrast to physical approaches based on very complex differential equations, our model derives functional dependencies directly from the observations. Hereby, we formulate the prediction task as regression problem and test different regression techniques such as linear regression, k-nearest neighbors and support vector regression. In our experiments, we analyze predictions for individual turbines as well as entire wind parks and show that a machine learning approach yields feasible results for short-term wind power prediction.
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Acknowledgments
We thank the presidential chair of the University of Oldenburg, the EWE research institute NextEnergy, and the Ministry of Science and Culture of Lower Saxony for partly supporting this work. Further, we thank the US National Renewable Energy Laboratory (NREL) for providing the wind data set.
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Treiber, N.A., Heinermann, J., Kramer, O. (2016). Wind Power Prediction with Machine Learning. In: Lässig, J., Kersting, K., Morik, K. (eds) Computational Sustainability. Studies in Computational Intelligence, vol 645. Springer, Cham. https://doi.org/10.1007/978-3-319-31858-5_2
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