Abstract
The increasing volatility introduced to power grids by renewable energy sources makes it necessary that the accuracy of energy forecasts are improved. Photovoltaic (PV) power plants hold the biggest share of installed capacity of renewable energy in Germany, so that high quality PV power forecasts are vital for a cost efficient operation of the underlying electrical grid. In this paper, we evaluate multiple Numerical Weather Prediction (NWP) parameters for their ability to improve PV power forecasting features. The importance of features is decided by a Random Forest algorithm. Furthermore, the resulting top ranked features are tested by performing PV power forecasts with Support Vector Regression, Random Forest, and linear regression models.
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Notes
- 1.
meteocontrol GmbH: www.meteocontrol.com.
- 2.
European Centre for Medium-Range Weather Forecasts: www.ecmwf.int.
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Acknowledgements
We like to thank meteocontrol GmbH for providing the PV system measurements as well as the ECMWF for numerical weather prediction forecasts that are basis of our experimental analysis. Björn Wolff is funded by the PhD program “System Integration of Renewable Energies” (SEE) of the University of Oldenburg promoted by the Lower Saxony Ministry for Science and Culture (MWK).
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Wolff, B., Kramer, O., Heinemann, D. (2017). Selection of Numerical Weather Forecast Features for PV Power Predictions with Random Forests. 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_8
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