IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i12p2746-d1173390.html
   My bibliography  Save this article

Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model

Author

Listed:
  • Jialin Liu

    (School of Qianhu, Nanchang University, Nanchang 330031, China)

  • Chen Gong

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Suhua Chen

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Nanrun Zhou

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

Abstract

Precise and dependable wind speed forecasting (WSF) enables operators of wind turbines to make informed decisions and maximize the use of available wind energy. This study proposes a hybrid WSF model based on outlier correction, heuristic algorithms, signal decomposition methods, and DLinear. Specifically, the hybrid model (HI-IVMD-DLinear) comprises the Hampel identifier (HI), the improved variational mode decomposition (IVMD) optimized by grey wolf optimization (GWO), and DLinear. Firstly, outliers in the wind speed sequence are detected and replaced with the HI to mitigate their impact on prediction accuracy. Next, the HI-processed sequence is decomposed into multiple sub-sequences with the IVMD to mitigate the non-stationarity and fluctuations. Finally, each sub-sequence is predicted by the novel DLinear algorithm individually. The predictions are reconstructed to obtain the final wind speed forecast. The HI-IVMD-DLinear is utilized to predict the real historical wind speed sequences from three regions so as to assess its performance. The experimental results reveal the following findings: (a) HI could enhance prediction accuracy and mitigate the adverse effects of outliers; (b) IVMD demonstrates superior decomposition performance; (c) DLinear has great prediction performance and is suited to WSF; and (d) overall, the HI-IVMD-DLinear exhibits superior precision and stability in one-to-four-step-ahead forecasting, highlighting its vast potential for application.

Suggested Citation

  • Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2746-:d:1173390
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/12/2746/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/12/2746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    2. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    3. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    4. Liu, Hui & Mi, Xiwei & Li, Yanfei & Duan, Zhu & Xu, Yinan, 2019. "Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression," Renewable Energy, Elsevier, vol. 143(C), pages 842-854.
    5. Emeksiz, Cem & Tan, Mustafa, 2022. "Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach," Energy, Elsevier, vol. 238(PA).
    6. Liu, Hui & Chen, Chao & Tian, Hong-qi & Li, Yan-fei, 2012. "A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks," Renewable Energy, Elsevier, vol. 48(C), pages 545-556.
    7. Xiang Ying & Keke Zhao & Zhiqiang Liu & Jie Gao & Dongxiao He & Xuewei Li & Wei Xiong, 2022. "Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
    8. Shang, Zhihao & He, Zhaoshuang & Chen, Yao & Chen, Yanhua & Xu, MingLiang, 2022. "Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization," Energy, Elsevier, vol. 238(PC).
    9. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
    10. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    11. Zhang, Jinliang & Wei, Yiming & Tan, Zhongfu, 2020. "An adaptive hybrid model for short term wind speed forecasting," Energy, Elsevier, vol. 190(C).
    12. Bommidi, Bala Saibabu & Teeparthi, Kiran & Kosana, Vishalteja, 2023. "Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function," Energy, Elsevier, vol. 265(C).
    13. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
    14. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    15. SantamarĂ­a-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    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. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    2. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    3. Hong, Ying-Yi & Satriani, Thursy Rienda Aulia, 2020. "Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network," Energy, Elsevier, vol. 209(C).
    4. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    5. Wu, Jie & Li, Na & Zhao, Yan & Wang, Jujie, 2022. "Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting," Energy, Elsevier, vol. 242(C).
    6. Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    7. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    8. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Li, Zhuo, 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method," Renewable Energy, Elsevier, vol. 179(C), pages 1925-1939.
    9. Yang, Rui & Liu, Hui & Nikitas, Nikolaos & Duan, Zhu & Li, Yanfei & Li, Ye, 2022. "Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach," Energy, Elsevier, vol. 239(PB).
    10. Tahmasebifar, Reza & Moghaddam, Mohsen Parsa & Sheikh-El-Eslami, Mohammad Kazem & Kheirollahi, Reza, 2020. "A new hybrid model for point and probabilistic forecasting of wind power," Energy, Elsevier, vol. 211(C).
    11. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    12. Acikgoz, Hakan & Budak, Umit & Korkmaz, Deniz & Yildiz, Ceyhun, 2021. "WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network," Energy, Elsevier, vol. 233(C).
    13. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    14. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    15. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
    16. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    17. Wang, Xuguang & Li, Xiao & Su, Jie, 2023. "Distribution drift-adaptive short-term wind speed forecasting," Energy, Elsevier, vol. 273(C).
    18. Wang, Jianzhou & Niu, Tong & Lu, Haiyan & Guo, Zhenhai & Yang, Wendong & Du, Pei, 2018. "An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms," Applied Energy, Elsevier, vol. 211(C), pages 492-512.
    19. Jiang, Ping & Liu, Zhenkun & Niu, Xinsong & Zhang, Lifang, 2021. "A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting," Energy, Elsevier, vol. 217(C).
    20. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.

    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:jmathe:v:11:y:2023:i:12:p:2746-:d:1173390. 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.