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Applying Wavelet Filters in Wind Forecasting Methods

Author

Listed:
  • José A. Domínguez-Navarro

    (Department of Electrical Engineering, EINA, University of Zaragoza, 50018 Zaragoza, Spain)

  • Tania B. Lopez-Garcia

    (Department of Electrical Engineering, EINA, University of Zaragoza, 50018 Zaragoza, Spain)

  • Sandra Minerva Valdivia-Bautista

    (Centro Universitario de Ciencias e Ingenierías (CUCEI), Universidad de Guadalajara (UDG), Guadalajara 44160, Mexico)

Abstract

Wind is a physical phenomenon with uncertainties in several temporal scales, in addition, measured wind time series have noise superimposed on them. These time series are the basis for forecasting methods. This paper studied the application of the wavelet transform to three forecasting methods, namely, stochastic, neural network, and fuzzy, and six wavelet families. Wind speed time series were first filtered to eliminate the high-frequency component using wavelet filters and then the different forecasting methods were applied to the filtered time series. All methods showed important improvements when the wavelet filter was applied. It is important to note that the application of the wavelet technique requires a deep study of the time series in order to select the appropriate family and filter level. The best results were obtained with an optimal filtering level and improper selection may significantly affect the accuracy of the results.

Suggested Citation

  • José A. Domínguez-Navarro & Tania B. Lopez-Garcia & Sandra Minerva Valdivia-Bautista, 2021. "Applying Wavelet Filters in Wind Forecasting Methods," Energies, MDPI, vol. 14(11), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3181-:d:564900
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    References listed on IDEAS

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    Cited by:

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    3. Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
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    5. Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.

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