IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v286y2024ics0360544223029985.html
   My bibliography  Save this article

A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm

Author

Listed:
  • Li, Yanhui
  • Sun, Kaixuan
  • Yao, Qi
  • Wang, Lin

Abstract

Accurate wind speed forecasting is capable of increasing the stability of wind power system. Notably, there are numerous factors affecting wind speed, thus causing wind speed forecasting to be difficult. To address the above-mentioned challenge, a novel hybrid model integrating genetic algorithm (GA), variational mode decomposition (VMD), improved dung beetle optimization algorithm (IDBO), and Bidirectional long short-term memory network based on attention mechanism (BiLSTM-A) is proposed in this study to achieve satisfactory forecasting performance. In the proposed model, GA is adopted to optimize the VMD to eliminate noise and extract original series attributes. And the IDBO is adopted for hyperparameters selection for the BiLSTM-A. The proposed GA-VMD-IDBO-BiLSTM-A is compared with nine established comparable models, with the aim of verifying its forecasting performance. A series of experiments on four 1-hour real wind series in Stratford are performed to assess the performance of the model. The MAPE of the four datasets forecasting results reached 1.4%, 2.4%, 3.5%, 2.4%. As indicated by the experimental results, GA-VMD can better process the data and improve the forecasting accuracy. IDBO can optimize the parameters of BiLSTM model and improve the forecasting performance. The dual-optimization wind speed forecasting model can obtain high accuracy and strong stability.

Suggested Citation

  • Li, Yanhui & Sun, Kaixuan & Yao, Qi & Wang, Lin, 2024. "A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029985
    DOI: 10.1016/j.energy.2023.129604
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223029985
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129604?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. Liu, Hui & Mi, Xiwei & Li, Yanfei, 2018. "An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm," Renewable Energy, Elsevier, vol. 123(C), pages 694-705.
    2. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    3. 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).
    4. Yue‐Jun Zhang & Jin‐Liang Zhang, 2018. "Volatility forecasting of crude oil market: A new hybrid method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 781-789, December.
    5. Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast," Renewable Energy, Elsevier, vol. 164(C), pages 1508-1526.
    6. 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).
    7. Joseph, Lionel P. & Deo, Ravinesh C. & Prasad, Ramendra & Salcedo-Sanz, Sancho & Raj, Nawin & Soar, Jeffrey, 2023. "Near real-time wind speed forecast model with bidirectional LSTM networks," Renewable Energy, Elsevier, vol. 204(C), pages 39-58.
    8. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    9. Sobolewski, Robert Adam & Tchakorom, Médane & Couturier, Raphaël, 2023. "Gradient boosting-based approach for short- and medium-term wind turbine output power prediction," Renewable Energy, Elsevier, vol. 203(C), pages 142-160.
    10. Wang, Jian & Yang, Zhongshan, 2021. "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, Elsevier, vol. 171(C), pages 1418-1435.
    11. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    12. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    13. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    14. 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.
    15. 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.
    16. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    17. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    18. Suo, Leiming & Peng, Tian & Song, Shihao & Zhang, Chu & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad, 2023. "Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm," Energy, Elsevier, vol. 276(C).
    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. 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.
    21. Niu, Dongxiao & Sun, Lijie & Yu, Min & Wang, Keke, 2022. "Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model," Energy, Elsevier, vol. 254(PA).
    22. 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).
    23. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
    24. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    25. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    26. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
    27. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Xuehan & Wang, Wei & Ye, Lingling & Ren, Guorui & Fang, Fang & Liu, Jizhen & Chen, Zhe & Zhou, Qiang, 2024. "Improving frequency regulation ability for a wind-thermal power system by multi-objective optimized sliding mode control design," Energy, Elsevier, vol. 300(C).

    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. 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).
    3. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    4. 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).
    5. Zhang, Chu & Qiao, Xiujie & Zhang, Zhao & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator," Energy, Elsevier, vol. 295(C).
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).
    11. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    12. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).
    13. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    14. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    15. 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).
    16. Wang, Shuangxin & Shi, Jiarong & Yang, Wei & Yin, Qingyan, 2024. "High and low frequency wind power prediction based on Transformer and BiGRU-Attention," Energy, Elsevier, vol. 288(C).
    17. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    18. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    19. Zhang, Chu & Li, Zhengbo & Ge, Yida & Liu, Qianlong & Suo, Leiming & Song, Shihao & Peng, Tian, 2024. "Enhancing short-term wind speed prediction based on an outlier-robust ensemble deep random vector functional link network with AOA-optimized VMD," Energy, Elsevier, vol. 296(C).
    20. Wu, Binrong & Wang, Lin, 2024. "Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting," Energy, Elsevier, vol. 288(C).

    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:286:y:2024:i:c:s0360544223029985. 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.