Abstract
A reliable prediction of photovoltaic (PV) power plays an important part as basis for operation and management strategies for a efficient and economical integration into the power grid. Due to changing weather conditions, e.g., clouds and fog, a precise forecast in a few hour range can be a difficult task. The growing IT infrastructure allows a fine screening of PV power. On the basis of big data sets of PV measurements, we apply methods from statistical learning for one- to six-hour ahead predictions based on data with hourly resolution. In this work, we employ nearest neighbor regression and support vector regression for PV power predictions based on measurements and numerical weather predictions. We put an emphasis on the analysis of feature combinations based on these two data sources. After optimizing the settings and comparing the employed statistical learning models, we build a hybrid predictor that uses forecasts of both employed models.
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Acknowledgments
We thank Meteocontrol—Energy and Weather Services (http://www.meteocontrol.com) for providing the PV system measurements, the ECMWF for the numerical weather predictions that are basis of our experimental analysis and the Lower Saxony Ministry for Science and Culture (MWK) for promoting the Ph.D. program “System Integration of Renewable Energies” (SEE) of the University of Oldenburg.
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Wolff, B., Lorenz, E., Kramer, O. (2016). Statistical Learning for Short-Term Photovoltaic Power Predictions. 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_3
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DOI: https://doi.org/10.1007/978-3-319-31858-5_3
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