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A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting

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  • Jianzhong Zhou

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China)

  • Na Sun

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China)

  • Benjun Jia

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China)

  • Tian Peng

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China)

Abstract

Due to inherent randomness and fluctuation of wind speeds, it is very challenging to develop an effective and practical model to achieve accurate wind speed forecasting, especially over large forecasting horizons. This paper presents a new decomposition-optimization model created by integrating Variational Mode Decomposition (VMD), Backtracking Search Algorithm (BSA), and Regularized Extreme Learning Machine (RELM) to enhance forecasting accuracy. The observed wind speed time series is firstly decomposed by VMD into several relative stable subsequences. Then, an emerging optimization algorithm, BSA, is utilized to search the optimal parameters of the RELM. Subsequently, the well-trained RELM is constructed to do multi-step (1-, 2-, 4-, and 6-step) wind speed forecasting. Experiments have been executed with the proposed method as well as several benchmark models using several datasets from a widely-studied wind farm, Sotavento Galicia in Spain. Additionally, the effects of decomposition and optimization methods on the final forecasting results are analyzed quantitatively, whereby the importance of decomposition technique is emphasized. Results reveal that the proposed VMD-BSA-RELM model achieves significantly better performance than its rivals both on single- and multi-step forecasting with at least 50% average improvement, which indicates it is a powerful tool for short-term wind speed forecasting.

Suggested Citation

  • Jianzhong Zhou & Na Sun & Benjun Jia & Tian Peng, 2018. "A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 11(7), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1752-:d:156128
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    References listed on IDEAS

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    Cited by:

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    3. Yuxing Li & Xiao Chen & Jing Yu & Xiaohui Yang & Huijun Yang, 2019. "The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy," Energies, MDPI, vol. 12(3), pages 1-18, January.
    4. David Schönheit & Dominik Möst, 2019. "The Effect of Offshore Wind Capacity Expansion on Uncertainties in Germany’s Day-Ahead Wind Energy Forecasts," Energies, MDPI, vol. 12(13), pages 1-23, July.
    5. 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.
    6. Sun, Na & Zhou, Jianzhong & Chen, Lu & Jia, Benjun & Tayyab, Muhammad & Peng, Tian, 2018. "An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine," Energy, Elsevier, vol. 165(PB), pages 939-957.
    7. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    8. Jian Yang & Xin Zhao & Haikun Wei & Kanjian Zhang, 2019. "Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction," Energies, MDPI, vol. 12(3), pages 1-12, January.
    9. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    10. Qinkai Han & Hao Wu & Tao Hu & Fulei Chu, 2018. "Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models," Energies, MDPI, vol. 11(11), pages 1-23, November.
    11. Hassan, Bryar A. & Rashid, Tarik A., 2020. "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Applied Mathematics and Computation, Elsevier, vol. 370(C).

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