Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction

Bogdan Bochenek, Jakub Jurasz, Adam Jaczewski, Gabriel Stachura, Piotr Sekuła, Tomasz Strzyżewski, Marcin Wdowikowski and Mariusz Figurski
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Bogdan Bochenek: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland
Jakub Jurasz: Faculty of Environmental Engineering, Wrocław University of Science and Technology, 50-377 Wrocław, Poland
Adam Jaczewski: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland
Gabriel Stachura: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland
Piotr Sekuła: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland
Tomasz Strzyżewski: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland
Marcin Wdowikowski: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland
Mariusz Figurski: Institute of Meteorology and Water Management—National Research Institute, 01-673 Warsaw, Poland

Energies, 2021, vol. 14, issue 8, 1-18

Abstract: The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability. Taking into account the nature of the power system, it is, therefore, imperative to predict their future energy generation to economically schedule the use of conventional generators. Considering the above, this paper examines the possibility to predict day-ahead wind power based on different machine learning methods not for a specific wind farm but at national level. A numerical weather prediction model used operationally in the Institute of Meteorology and Water Management–National Research Institute in Poland and hourly data of recorded wind power generation in Poland were used for forecasting models creation and testing. With the best method, the Extreme Gradient Boosting, and two years of training (2018–2019), the day-ahead, hourly wind power generation in Poland in 2020 was predicted with 26.7% mean absolute percentage error and 4.5% root mean square error accuracy. Seasonal and daily differences in predicted error were found, showing high mean absolute percentage error in summer and during daytime.

Keywords: machine learning; wind power forecasting; day-ahead; numerical weather prediction; ALARO (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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