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An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed

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  • Xiong, Xiong
  • Zou, Ruilin
  • Sheng, Tao
  • Zeng, Weilin
  • Ye, Xiaoling

Abstract

The WRF model cannot produce an accurate wind speed prediction to guarantee the steady operation of wind farms due to the inability of wind farms to acquire meteorological information and imperfections in physical schemes. This paper suggested an ultra-short-term wind speed correction method based on wind speed fluctuation characteristics, known as VSDA-BO-LSTM, in order to increase the accuracy of wind speed prediction from WRF model. First, the Volatile Stable Day model (VSDA), which separated the original wind speed data into segments, extracted fluctuation features, and categorized them, was proposed. This model successfully addressed the lack of common wind speed feature extraction and wind speed feature stripping. Then, the long-short-term memory (LSTM) deep learning algorithm combined with the output of the VSDA model was used to correct the wind speed prediction value of the WRF model. At the same time, the Bayesian optimization algorithm was used to determine the parameters of LSTM.The results of the experiment demonstrated the suggested method’s good capability for wind speed correction. Moreover, the proposed method was very competitive compared to the current state-of-the-art model.

Suggested Citation

  • Xiong, Xiong & Zou, Ruilin & Sheng, Tao & Zeng, Weilin & Ye, Xiaoling, 2023. "An ultra-short-term wind speed correction method based on the fluctuation characteristics of wind speed," Energy, Elsevier, vol. 283(C).
  • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223024064
    DOI: 10.1016/j.energy.2023.129012
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    1. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
    2. Zjavka, Ladislav, 2015. "Wind speed forecast correction models using polynomial neural networks," Renewable Energy, Elsevier, vol. 83(C), pages 998-1006.
    3. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    4. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
    5. Prósper, Miguel A. & Otero-Casal, Carlos & Fernández, Felipe Canoura & Miguez-Macho, Gonzalo, 2019. "Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations," Renewable Energy, Elsevier, vol. 135(C), pages 674-686.
    6. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
    7. Xu, Weifeng & Liu, Pan & Cheng, Lei & Zhou, Yong & Xia, Qian & Gong, Yu & Liu, Yini, 2021. "Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy," Renewable Energy, Elsevier, vol. 163(C), pages 772-782.
    8. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    9. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    10. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    11. Ambach, Daniel & Schmid, Wolfgang, 2017. "A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting," Energy, Elsevier, vol. 135(C), pages 833-850.
    12. Morrison, Rory & Liu, Xiaolei & Lin, Zi, 2022. "Anomaly detection in wind turbine SCADA data for power curve cleaning," Renewable Energy, Elsevier, vol. 184(C), pages 473-486.
    13. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
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    1. Yang, Mao & Guo, Yunfeng & Fan, Fulin & Huang, Tao, 2024. "Two-stage correction prediction of wind power based on numerical weather prediction wind speed superposition correction and improved clustering," Energy, Elsevier, vol. 302(C).

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