IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v7y2025i2p27-d1675947.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/7/2/27/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/7/2/27/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. Dongxiao Niu & Di Pu & Shuyu Dai, 2018. "Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm," Energies, MDPI, vol. 11(5), pages 1-21, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    2. 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.
    3. Li, Yanting & Wu, Zhenyu & Su, Yan, 2023. "Adaptive short-term wind power forecasting with concept drifts," Renewable Energy, Elsevier, vol. 217(C).
    4. 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.
    5. 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.
    6. Saeed Hayati & Kenji Fukumizu & Afshin Parvardeh, 2024. "Kernel mean embedding of probability measures and its applications to functional data analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 447-484, June.
    7. Azar, Pablo D. & Micali, Silvio, 2018. "Computational principal agent problems," Theoretical Economics, Econometric Society, vol. 13(2), May.
    8. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    9. Luis A Barboza & Shu-Wei Chou-Chen & Paola Vásquez & Yury E García & Juan G Calvo & Hugo G Hidalgo & Fabio Sanchez, 2023. "Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 17(1), pages 1-13, January.
    10. Ming Wei & Xue-yi You, 2022. "Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4003-4018, September.
    11. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    12. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    13. Basora, Luis & Viens, Arthur & Chao, Manuel Arias & Olive, Xavier, 2025. "A benchmark on uncertainty quantification for deep learning prognostics," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    14. R de Fondeville & A C Davison, 2018. "High-dimensional peaks-over-threshold inference," Biometrika, Biometrika Trust, vol. 105(3), pages 575-592.
    15. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    16. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    17. Finn Lindgren, 2015. "Comments on: Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 35-44, March.
    18. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    19. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    20. Kelly Trinh & Bo Zhang & Chenghan Hou, 2025. "Macroeconomic real‐time forecasts of univariate models with flexible error structures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 59-78, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:7:y:2025:i:2:p:27-:d:1675947. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.