IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v271y2023ics0360544223004000.html
   My bibliography  Save this article

Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization

Author

Listed:
  • Zhu, Qiannan
  • Jiang, Feng
  • Li, Chaoshun

Abstract

Wind power (WP) interval prediction has attracted more and more attention in recent years due to WP's intermittency and uncertainty. However, traditional interval prediction models suffer from data distribution assumptions, fixed interval widths, etc. This paper proposes a hybrid WP interval prediction method to eliminate these constraints. First, the mean impact value is applied to select the optimal inputs from meteorological factors, which are considered together with the WP data. Then, we propose a time-varying interval optimization strategy to construct prediction intervals (PIs) and avoid the restrictive condition of data distribution, which can provide a time-varying interval width simultaneously. Meanwhile a convolutional gated recurrent unit is performed to extract the spatial-temporal features and generates the prediction results. Based on these results, the final PIs are generated by the proposed multi-objective elephant clan optimization, in which three objectives are optimized simultaneously. To evaluate the performance of the proposed model, two WP datasets in Ningxia, China, are used. Finally, the obtained PIs are applied for decision-making to offer planned productions in the future and estimate the operational costs. The comparison results indicate that the proposed model can provide high-quality PIs and achieve lower operational costs than the benchmark models in decision-making.

Suggested Citation

  • Zhu, Qiannan & Jiang, Feng & Li, Chaoshun, 2023. "Time-varying interval prediction and decision-making for short-term wind power using convolutional gated recurrent unit and multi-objective elephant clan optimization," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004000
    DOI: 10.1016/j.energy.2023.127006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223004000
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127006?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. Naik, Jyotirmayee & Dash, Pradipta Kishore & Dhar, Snehamoy, 2019. "A multi-objective wind speed and wind power prediction interval forecasting using variational modes decomposition based Multi-kernel robust ridge regression," Renewable Energy, Elsevier, vol. 136(C), pages 701-731.
    2. Liu, Tongxiang & Zhao, Qiujun & Wang, Jianzhou & Gao, Yuyang, 2021. "A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations," Renewable Energy, Elsevier, vol. 163(C), pages 88-104.
    3. Xiyun Yang & Guo Fu & Yanfeng Zhang & Ning Kang & Feng Gao, 2017. "A Naive Bayesian Wind Power Interval Prediction Approach Based on Rough Set Attribute Reduction and Weight Optimization," Energies, MDPI, vol. 10(11), pages 1-15, November.
    4. Liu, Hongyi & Han, Hua & Sun, Yao & Shi, Guangze & Su, Mei & Liu, Zhangjie & Wang, Hongfei & Deng, Xiaofei, 2022. "Short-term wind power interval prediction method using VMD-RFG and Att-GRU," Energy, Elsevier, vol. 251(C).
    5. Tsao, Hao-Han & Leu, Yih-Guang & Chou, Li-Fen, 2021. "A center-of-concentrated-based prediction interval for wind power forecasting," Energy, Elsevier, vol. 237(C).
    6. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    7. Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
    8. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Jin, Yu, 2022. "A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization," Energy, Elsevier, vol. 239(PE).
    9. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(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. Dongran Song & Xiao Tan & Qian Huang & Li Wang & Mi Dong & Jian Yang & Solomin Evgeny, 2024. "Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023," Energies, MDPI, vol. 17(6), pages 1-22, March.
    2. 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).
    3. Rubio, José de Jesús & Garcia, Donaldo & Sossa, Humberto & Garcia, Ivan & Zacarias, Alejandro & Mujica-Vargas, Dante, 2023. "Energy processes prediction by a convolutional radial basis function network," Energy, Elsevier, vol. 284(C).
    4. Hua Yang & Xingquan Deng & Hao Shen & Qingfeng Lei & Shuxiang Zhang & Neng Liu, 2023. "Disease Detection and Identification of Rice Leaf Based on Improved Detection Transformer," Agriculture, MDPI, vol. 13(7), pages 1-17, July.
    5. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(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. Dong, Xianzhou & Guo, Weiyong & Zhou, Cheng & Luo, Yongqiang & Tian, Zhiyong & Zhang, Limao & Wu, Xiaoying & Liu, Baobing, 2024. "Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods," Energy, Elsevier, vol. 311(C).
    2. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao & Xie, Yuying & Liu, Fangjie, 2022. "A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution," Energy, Elsevier, vol. 238(PC).
    3. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    4. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    5. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    6. Ai, Chunyu & He, Shan & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm," Energy, Elsevier, vol. 278(C).
    7. Xinyue Fu & Zhongkai Feng & Xinru Yao & Wenjie Liu, 2023. "A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction," Energies, MDPI, vol. 16(15), pages 1-23, July.
    8. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    9. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    10. 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.
    11. Zhang, Haipeng & Wang, Jianzhou & Qian, Yuansheng & Li, Qiwei, 2024. "Point and interval wind speed forecasting of multivariate time series based on dual-layer LSTM," Energy, Elsevier, vol. 294(C).
    12. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    13. Wang, Yun & Chen, Tuo & Zou, Runmin & Song, Dongran & Zhang, Fan & Zhang, Lingjun, 2022. "Ensemble probabilistic wind power forecasting with multi-scale features," Renewable Energy, Elsevier, vol. 201(P1), pages 734-751.
    14. Li, Kang & Duan, Pengfei & Cao, Xiaodong & Cheng, Yuanda & Zhao, Bingxu & Xue, Qingwen & Feng, Mengdan, 2024. "A multi-energy load forecasting method based on complementary ensemble empirical model decomposition and composite evaluation factor reconstruction," Applied Energy, Elsevier, vol. 365(C).
    15. 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).
    16. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
    17. He, Yaoyao & Cao, Chaojin & Wang, Shuo & Fu, Hong, 2022. "Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems," Applied Energy, Elsevier, vol. 322(C).
    18. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    19. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    20. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(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:271:y:2023:i:c:s0360544223004000. 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.