IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v269y2023ics0360544223001329.html
   My bibliography  Save this article

A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism

Author

Listed:
  • Yu, Min
  • Niu, Dongxiao
  • Gao, Tian
  • Wang, Keke
  • Sun, Lijie
  • Li, Mingyu
  • Xu, Xiaomin

Abstract

With resource shortages and global warming becoming increasingly serious, it is urgent to accelerate the transition to green and low-carbon energy. Wind power, as a kind of green, low-carbon, zero-cost renewable energy, has undergone rapid development. Aiming to address the problem of strong randomness and strong temporal correlations in wind power prediction (WPP), a new framework for WPP based on RF-WOA-VMD and BiGRU optimized by an attention mechanism is proposed. Firstly, the random forest algorithm (RF) is adopted to screen the influencing factors of wind power, effectively reducing the data redundancy and improving the prediction efficiency. Secondly, the variational modal decomposition (VMD) algorithm optimized by the whale algorithm (WOA) for WPP is adopted, which uses the WOA to adaptively determine the optimal parameters [K, α] in VMD, adaptively decompose raw wind power series, and reduce data noise. Furthermore, the BiGRU algorithm optimized by the attention mechanism is proposed for WPP. The attention mechanism is introduced to assign different weights to the hidden states of BiGRU to emphasize the impact of key information. Ultimately, the experimental result illustrated that the proposed model further enhances the prediction accuracy. According to data set 1, MAPE is reduced by 86.81% compared with BiGRU.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223001329
    DOI: 10.1016/j.energy.2023.126738
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223001329
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126738?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Mucun & Feng, Cong & Zhang, Jie, 2019. "Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation," Applied Energy, Elsevier, vol. 256(C).
    2. 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.
    3. Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
    4. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    5. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    6. 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.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    8. Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, Elsevier, vol. 75(C), pages 301-307.
    9. Ohba, Masamichi & Kadokura, Shinji & Nohara, Daisuke, 2016. "Impacts of synoptic circulation patterns on wind power ramp events in East Japan," Renewable Energy, Elsevier, vol. 96(PA), pages 591-602.
    10. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    11. Dorado-Moreno, M. & Cornejo-Bueno, L. & Gutiérrez, P.A. & Prieto, L. & Hervás-Martínez, C. & Salcedo-Sanz, S., 2017. "Robust estimation of wind power ramp events with reservoir computing," Renewable Energy, Elsevier, vol. 111(C), pages 428-437.
    12. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
    13. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    14. de Jong, Pieter & Kiperstok, Asher & Sánchez, Antonio Santos & Dargaville, Roger & Torres, Ednildo Andrade, 2016. "Integrating large scale wind power into the electricity grid in the Northeast of Brazil," Energy, Elsevier, vol. 100(C), pages 401-415.
    15. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    16. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
    17. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    18. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    19. 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).
    20. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
    21. 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.
    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. 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).
    2. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    3. 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).
    4. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    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. 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.
    7. 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.
    8. Liu, Guangbiao & Zhou, Jianzhong & Jia, Benjun & He, Feifei & Yang, Yuqi & Sun, Na, 2019. "Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method," Applied Energy, Elsevier, vol. 238(C), pages 643-667.
    9. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    10. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    11. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    12. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
    13. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    14. Pei Du & Yu Jin & Kequan Zhang, 2016. "A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed," Sustainability, MDPI, vol. 8(8), pages 1-25, August.
    15. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
    16. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    17. 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.
    18. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    19. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    20. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.

    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:eee:energy:v:269:y:2023:i:c:s0360544223001329. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.