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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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    1. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    2. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    3. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    4. Correia, J.M. & Bastos, A. & Brito, M.C. & Trigo, R.M., 2017. "The influence of the main large-scale circulation patterns on wind power production in Portugal," Renewable Energy, Elsevier, vol. 102(PA), pages 214-223.
    5. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
    6. Hugo Algarvio & Fernando Lopes & António Couto & Ana Estanqueiro, 2019. "Participation of wind power producers in day‐ahead and balancing markets: An overview and a simulation‐based study," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    7. 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.
    8. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Li, Zhuo, 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method," Renewable Energy, Elsevier, vol. 179(C), pages 1925-1939.
    9. Nantian Huang & Enkai Xing & Guowei Cai & Zhiyong Yu & Bin Qi & Lin Lin, 2018. "Short-Term Wind Speed Forecasting Based on Low Redundancy Feature Selection," Energies, MDPI, vol. 11(7), pages 1-19, June.
    10. Forbes, Kevin F. & Zampelli, Ernest M., 2020. "Accuracy of wind energy forecasts in Great Britain and prospects for improvement," Utilities Policy, Elsevier, vol. 67(C).
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    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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