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

Author

Listed:
  • 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
    Faculty of Physics and Applied Computer Science, AGH University, 30-059 Krakow, 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)

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.

Suggested Citation

  • Bogdan Bochenek & Jakub Jurasz & Adam Jaczewski & Gabriel Stachura & Piotr Sekuła & Tomasz Strzyżewski & Marcin Wdowikowski & Mariusz Figurski, 2021. "Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2164-:d:535292
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    References listed on IDEAS

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    2. Couto, António & Estanqueiro, Ana, 2022. "Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks," Renewable Energy, Elsevier, vol. 201(P1), pages 1076-1085.
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    5. Cabello-López, Tomás & Carranza-García, Manuel & Riquelme, José C. & García-Gutiérrez, Jorge, 2023. "Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level," Applied Energy, Elsevier, vol. 350(C).
    6. Katarzyna Kubiak-Wójcicka & Filip Polak & Leszek Szczęch, 2022. "Water Power Plants Possibilities in Powering Electric Cars—Case Study: Poland," Energies, MDPI, vol. 15(4), pages 1-17, February.
    7. Jonkers, Jef & Avendano, Diego Nieves & Van Wallendael, Glenn & Van Hoecke, Sofie, 2024. "A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests," Applied Energy, Elsevier, vol. 361(C).
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    9. Katarzyna Kubiak-Wójcicka & Leszek Szczęch, 2021. "Dynamics of Electricity Production against the Backdrop of Climate Change: A Case Study of Hydropower Plants in Poland," Energies, MDPI, vol. 14(12), pages 1-24, June.

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