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Forecasting and Multilevel Early Warning of Wind Speed Using an Adaptive Kernel Estimator and Optimized Gated Recurrent Units

Author

Listed:
  • Pengjiao Wang

    (School of Civil Engineering, Central South University, Changsha 410075, China)

  • Qiuliang Long

    (School of Civil Engineering, Central South University, Changsha 410075, China
    Hunan Harbor Engineering Corporation Limited, Changsha 410021, China)

  • Hu Zhang

    (Hunan Harbor Engineering Corporation Limited, Changsha 410021, China)

  • Xu Chen

    (Hunan Harbor Engineering Corporation Limited, Changsha 410021, China)

  • Ran Yu

    (Hunan Harbor Engineering Corporation Limited, Changsha 410021, China)

  • Fengqi Guo

    (School of Civil Engineering, Central South University, Changsha 410075, China)

Abstract

Accurately predicting wind speeds is of great significance in various engineering applications, such as the operation of high-speed trains. Machine learning models are effective in this field. However, existing studies generally provide deterministic predictions and utilize decomposition techniques in advance to enhance predictive performance, which may encounter data leakage and fail to capture the stochastic nature of wind data. This work proposes an advanced framework for the prediction and early warning of wind speeds by combining the optimized gated recurrent unit (GRU) and adaptive kernel density estimator (AKDE). Firstly, 12 samples (26,280 points each) were collected from an extensive open database. Three representative metaheuristic algorithms were then employed to optimize the parameters of diverse models, including extreme learning machines, a transformer model, and recurrent networks. The results yielded an optimal selection using the GRU and the crested porcupine optimizer. Afterwards, by using the AKDE, the joint probability density and cumulative distribution function of wind predictions and related predicting errors could be obtained. It was then applicable to calculate the conditional probability that actual wind speed exceeds the critical value, thereby providing probabilistic-based predictions in a multilevel manner. A comparison of the predictive performance of various methods and accuracy of subsequent decisions validated the proposed framework.

Suggested Citation

  • Pengjiao Wang & Qiuliang Long & Hu Zhang & Xu Chen & Ran Yu & Fengqi Guo, 2024. "Forecasting and Multilevel Early Warning of Wind Speed Using an Adaptive Kernel Estimator and Optimized Gated Recurrent Units," Mathematics, MDPI, vol. 12(16), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2581-:d:1460969
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    References listed on IDEAS

    as
    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    3. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    4. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    5. 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.
    6. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
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