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A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Author

Listed:
  • Yiqi Chu

    (Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China)

  • Chengcai Li

    (Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China)

  • Yefang Wang

    (Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
    Baicheng Ordnance Test Center of China, Baicheng 137001, China)

  • Jing Li

    (Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China)

  • Jian Li

    (Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China)

Abstract

Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

Suggested Citation

  • Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:894-:d:81798
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    References listed on IDEAS

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    Cited by:

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    2. Francisco Martínez-Álvarez & Alicia Troncoso & José C. Riquelme, 2017. "Recent Advances in Energy Time Series Forecasting," Energies, MDPI, vol. 10(6), pages 1-3, June.

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