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Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting

Author

Listed:
  • Zhang, Fei
  • Li, Peng-Cheng
  • Gao, Lu
  • Liu, Yong-Qian
  • Ren, Xiao-Ying

Abstract

Wind prediction technology has been the focus on the national research with the basis of the power system planning, a reference for power dispatch, and the optimal power flow distribution. Prediction technology is moving into the direction of controlling refinement with the development of the information technology, the artificial intelligence technology, and the improvement of edge computing devices. The wind farms can use real-time wind power prediction to improve their overall efficiency through advanced planning of the wind turbine adjustment and the pre-setting of the yaw, pitch, and generation excitation control systems. This paper proposes an innovative autoregressive dynamic adaptive (ARDA) model based on the improvement of the autoregressive (AR) model. The fixed parameter estimation method of the AR model is improved in the proposed model to a dynamically adaptive stepwise parameter estimation method. Meanwhile, the coefficients of the model are updated adaptively based on the characteristics of wind power data, which improves the accuracy of the proposed model. The prediction accuracy of the proposed model is further improved by the residual function. It was observed that the model adapts well to wind power data with different degrees of volatility. The ARDA model and two other models were tested by using stationary and fluctuating wind power data (unit: seconds), and the wind power prediction results at different forecasting step lengths were compared. It was observed that the ARDA model is more accurate, with faster calculation rate, and better dynamic adaptability to data fluctuations than the ARIMA and LSTM models. This paper proposes an important method for real-time power prediction that can be employed for the advanced control and improved the power generation of wind farms.

Suggested Citation

  • Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
    • Handle: RePEc:eee:renene:v:169:y:2021:i:c:p:129-143
    DOI: 10.1016/j.renene.2021.01.003
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    5. Jie Liu & Quan Shi & Ruilian Han & Juan Yang, 2021. "A Hybrid GA–PSO–CNN Model for Ultra-Short-Term Wind Power Forecasting," Energies, MDPI, vol. 14(20), pages 1-22, October.
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    7. Cui, Xiwen & Yu, Xiaoyu & Niu, Dongxiao, 2024. "The ultra-short-term wind power point-interval forecasting model based on improved variational mode decomposition and bidirectional gated recurrent unit improved by improved sparrow search algorithm a," Energy, Elsevier, vol. 288(C).
    8. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    9. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
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