Abstract
Ensembles turn out to be excellent wind power prediction methods. But the space of algorithms and parameters of supervised learning ensembles is large. For an efficient optimization and tuning of ensembles, we propose to employ evolutionary multi-objective optimization methods in this work. NSGA-II is a classic optimization algorithm based on non-dominated sorting and maximization of the crowding distance and has successfully been applied in various applications in the past. The experimental part of the paper shows how NSGA-II tunes SVR ensembles, random forests, and heterogenous ensembles. The study demonstrates that the proposed approach evolves an attractive set of ensembles for a practitioner yielding numerous compromises of prediction accuracy and runtime.
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Acknowledgement
We thank the ministry of science and culture of Lower Saxony for supporting us with the PhD Program System Integration of Renewable Energies. Furthermore, we thank the US National Renewable Energy Laboratory for providing the wind dataset.
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Heinermann, J., Lässig, J., Kramer, O. (2017). Evolutionary Multi-objective Ensembles for Wind Power Prediction. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2016. Lecture Notes in Computer Science(), vol 10097. Springer, Cham. https://doi.org/10.1007/978-3-319-50947-1_9
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DOI: https://doi.org/10.1007/978-3-319-50947-1_9
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