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Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation

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  • Cheng, William Y.Y.
  • Liu, Yubao
  • Bourgeois, Alfred J.
  • Wu, Yonghui
  • Haupt, Sue Ellen

Abstract

In recent years, adopting renewable energy, such as wind power, has become a national energy policy for many countries due to concerns of pollution and climate change from fossil fuel consumption. However, accurate prediction of wind is crucial in managing the power load. Numerical weather prediction (NWP) models are essential tools for wind prediction, but they need accurate initial conditions in order to produce an accurate forecast. However, NWP models are not guaranteed to have accurate initial conditions over wind farms in isolated locations. This study hypothesizes that short-term, 0–3 h, wind forecast can be improved by assimilating anemometer wind speed observations from wind farm turbines into a numerical weather forecast system. A technique was developed to circumvent the requirement of simultaneously ingesting the wind speed and direction in a data assimilation/weather forecasting system. A six-day case study revealed that assimilating wind speed can improve the 0–3 h wind speed (power) forecast by reducing the mean absolute error up to 0.5–0.6 m s−1 (30–40%).

Suggested Citation

  • Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
    • Handle: RePEc:eee:renene:v:107:y:2017:i:c:p:340-351
    DOI: 10.1016/j.renene.2017.02.014
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    3. Omid Alizadeh, 2022. "Advances and challenges in climate modeling," Climatic Change, Springer, vol. 170(1), pages 1-26, January.
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    7. 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.
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    11. Mylonas, M.P. & Barbouchi, S. & Herrmann, H. & Nastos, P.T., 2018. "Sensitivity analysis of observational nudging methodology to reduce error in wind resource assessment (WRA) in the North Sea," Renewable Energy, Elsevier, vol. 120(C), pages 446-456.
    12. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    13. Li, Jingrui & Wang, Jianzhou & Zhang, Haipeng & Li, Zhiwu, 2022. "An innovative combined model based on multi-objective optimization approach for forecasting short-term wind speed: A case study in China," Renewable Energy, Elsevier, vol. 201(P1), pages 766-779.
    14. Rebecca J. Barthelmie & Tristan J. Shepherd & Jeanie A. Aird & Sara C. Pryor, 2020. "Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains," Energies, MDPI, vol. 13(16), pages 1-21, August.
    15. Yechi Zhang & Jianzhou Wang & Haiyan Lu, 2019. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting," Energies, MDPI, vol. 12(10), pages 1-27, May.
    16. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    17. Jianguo Zhou & Xiaolei Xu & Xuejing Huo & Yushuo Li, 2019. "Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    18. Liu, Yichao & Chen, Daoyi & Li, Sunwei & Chan, P.W., 2018. "Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling," Energy, Elsevier, vol. 160(C), pages 582-596.
    19. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
    20. Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
    21. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.

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