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Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction

Author

Listed:
  • Zifa Liu

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Xinyi Li

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Haiyan Zhao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Accurate wind power forecasting is an important factor in ensuring the stable operation of a power system. In this paper, we propose a wind power forecasting method based on feature analysis and error correction in order to further improve its accuracy. Firstly, the correlation analysis is carried out on the features using the maximal information coefficient (MIC), and the main features are selected as the model input items. Then, the two primary factors affecting wind power forecasting—the wind speed and wind direction provided by numerical weather prediction (NWP)—are analyzed, and the data are divided and clustered from the above two perspectives. Next, the bidirectional long short-term memory network (BiLSTM) is used to predict the power of each group of sub data. Finally, the error is forecasted by a light gradient boosting machine (LightGBM) in order to correct the prediction results. The calculation example shows that the proposed method achieves the expected purpose and improves the accuracy of forecasting effectively.

Suggested Citation

  • Zifa Liu & Xinyi Li & Haiyan Zhao, 2023. "Short-Term Wind Power Forecasting Based on Feature Analysis and Error Correction," Energies, MDPI, vol. 16(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4249-:d:1152754
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    References listed on IDEAS

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    Cited by:

    1. G. Ponkumar & S. Jayaprakash & Karthick Kanagarathinam, 2023. "Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis," Energies, MDPI, vol. 16(14), pages 1-24, July.

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