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Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation

Author

Listed:
  • Lei Zhang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Lun Xie

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qinkai Han

    (The State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China)

  • Zhiliang Wang

    (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Chen Huang

    (TAIJI Computer Corporations Limited, Beijing 100083, China)

Abstract

Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

Suggested Citation

  • Lei Zhang & Lun Xie & Qinkai Han & Zhiliang Wang & Chen Huang, 2020. "Probability Density Forecasting of Wind Speed Based on Quantile Regression and Kernel Density Estimation," Energies, MDPI, vol. 13(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6125-:d:449285
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    References listed on IDEAS

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    Cited by:

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    3. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "A novel spatial–temporal generative autoencoder for wind speed uncertainty forecasting," Energy, Elsevier, vol. 282(C).
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    5. Huazhu Xue & Hui Wu & Guotao Dong & Jianjun Gao, 2023. "A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

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