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Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks

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  • Couto, António
  • Estanqueiro, Ana

Abstract

Forecasting with accuracy the quantity of energy produced by wind power plants is crucial to enabling its optimal integration into power systems and electricity markets. Despite the remarkable improvements in the wind forecasting systems in recent years, large errors can still be observed, especially for longer time horizons. This work focuses on identifying new numerical weather prediction (NWP)-based features aiming to improve the overall quality of wind power forecasts.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:1076-1085
    DOI: 10.1016/j.renene.2022.11.022
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    References listed on IDEAS

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    Cited by:

    1. Nicholas Christakis & Ioanna Evangelou & Dimitris Drikakis & George Kossioris, 2024. "A Computational Methodology for Assessing Wind Potential," Energies, MDPI, vol. 17(6), pages 1-23, March.
    2. Yi Liu & Jun He & Yu Wang & Zong Liu & Lixun He & Yanyang Wang, 2023. "Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain," Energies, MDPI, vol. 16(14), pages 1-25, July.

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