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A novel algorithm system for wind power prediction based on RANSAC data screening and Seq2Seq-Attention-BiGRU model

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  • Zhou, Gaoyu
  • Hu, Guofeng
  • Zhang, Daxing
  • Zhang, Yun

Abstract

Accurate wind power prediction plays a crucial role in mitigating the challenges posed by the variability and fluctuations of wind power generation, while also assisting in the regulation of power grid peaks and voltages. This paper introduces a novel algorithm system for wind power prediction, which incorporates RANSAC noise screening and the Seq2Seq-Attention-BiGRU model to enhance prediction accuracy. To address the presence of large-scale noise datasets and the well-established correlation between wind speed and power, we propose the utilization of RANSAC for effective noise screening. Subsequently, the Seq2Seq-Attention wind power prediction model is developed and validated through comparisons with measured data from various wind farms across different seasons. Additionally, the BiGRU error correction model is introduced to further refine the wind power predictions. Comparative analyses between the proposed prediction method and existing forecasting techniques demonstrate the effectiveness of our approach in enhancing prediction accuracy. Specifically, when compared to the LSTM method, the RANSAC-Seq2Seq-Attention-BiGRU forecasting method yielded a remarkable decrease in the RMSE of 26.0%, 43.6%, 46.8%, and 48.4% for the months of January, March, June, and September, respectively. Moreover, the MAE exhibited reductions of 19.7%, 43.4%, 41.0%, and 46.2% for the same respective months.

Suggested Citation

  • Zhou, Gaoyu & Hu, Guofeng & Zhang, Daxing & Zhang, Yun, 2023. "A novel algorithm system for wind power prediction based on RANSAC data screening and Seq2Seq-Attention-BiGRU model," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223023800
    DOI: 10.1016/j.energy.2023.128986
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    References listed on IDEAS

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    1. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    2. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    3. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
    4. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    5. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    6. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    7. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    8. Qin, Yong & Li, Kun & Liang, Zhanhao & Lee, Brendan & Zhang, Fuyong & Gu, Yongcheng & Zhang, Lei & Wu, Fengzhi & Rodriguez, Dragan, 2019. "Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal," Applied Energy, Elsevier, vol. 236(C), pages 262-272.
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