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Wind Speed Forecasting with Differentially Evolved Minimum-Bandwidth Filters and Gated Recurrent Units

Author

Listed:
  • Khathutshelo Steven Sivhugwana

    (Department of Statistics, University of South Africa, Florida Campus, Johannesburg 1709, South Africa)

  • Edmore Ranganai

    (Department of Statistics, University of South Africa, Florida Campus, Johannesburg 1709, South Africa)

Abstract

Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends heavily on the decomposition level ( L ) and the wavelet filter technique selected. Hence, we examined the efficacy of wind predictions as a function of L and wavelet filters. In the proposed hybrid approach, differential evolution (DE) optimises the decomposition level of various wavelet filters (i.e., least asymmetric (LA), Daubechies (DB), and Morris minimum-bandwidth (MB)) using the maximal overlap discrete wavelet transform (MODWT), allowing for the decomposition of wind data into more statistically sound sub-signals. These sub-signals are used as inputs into the gated recurrent unit (GRU) to accurately capture wind speed. The final predicted values are obtained by reconciling the sub-signal predictions using multiresolution analysis (MRA) to form wavelet-MODWT-GRUs. Using wind data from three Wind Atlas South Africa (WASA) locations, Alexander Bay, Humansdorp, and Jozini, the root mean square error, mean absolute error, coefficient of determination, probability integral transform, pinball loss, and Dawid-Sebastiani showed that the MB-MODWT-GRU at L = 3 was best across the three locations.

Suggested Citation

  • Khathutshelo Steven Sivhugwana & Edmore Ranganai, 2025. "Wind Speed Forecasting with Differentially Evolved Minimum-Bandwidth Filters and Gated Recurrent Units," Forecasting, MDPI, vol. 7(2), pages 1-27, June.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:2:p:27-:d:1675947
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    References listed on IDEAS

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    1. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
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    3. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    4. 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.
    5. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
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    1. Khathutshelo Steven Sivhugwana & Edmore Ranganai, 2025. "Short-Term Forecasting of Unplanned Power Outages Using Machine Learning Algorithms: A Robust Feature Engineering Strategy Against Multicollinearity and Nonlinearity," Energies, MDPI, vol. 18(18), pages 1-38, September.

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