IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i7p1646-d1620247.html
   My bibliography  Save this article

Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC

Author

Listed:
  • Yan Yan

    (State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China)

  • Yong Qian

    (State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, China)

  • Yan Zhou

    (School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China)

Abstract

Accurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional forecasting methods mainly focus on modeling based on a single data source, which leads to an inability to fully capture the underlying relationships in wind power data. In addition, current models often lack dynamic adaptability to data characteristics, resulting in lower prediction accuracy and reliability under different time periods or weather conditions. To address the aforementioned issues, an ultra-short-term hybrid probabilistic prediction model based on MultiFusion, ChronoNet, and adaptive Monte Carlo (AMC) is proposed in this paper. By combining multi-source data fusion and a multiple-gated structure, the nonlinear characteristics and uncertainties of wind power under various input conditions are effectively captured by this model. Additionally, the AMC method is applied in this paper to provide comprehensive, accurate, and flexible ultra-short-term probabilistic predictions. Ultimately, experiments are conducted on multiple datasets, and the results show that the proposed model not only improves the accuracy of deterministic prediction but also enhances the reliability of probabilistic prediction intervals.

Suggested Citation

  • Yan Yan & Yong Qian & Yan Zhou, 2025. "Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC," Energies, MDPI, vol. 18(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1646-:d:1620247
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/7/1646/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/7/1646/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Sen & Zhang, Wenjie & Sun, Yonghui & Trivedi, Anupam & Chung, C.Y. & Srinivasan, Dipti, 2024. "Wind Power Forecasting in the presence of data scarcity: A very short-term conditional probabilistic modeling framework," Energy, Elsevier, vol. 291(C).
    2. Fabio Famoso & Ludovica Maria Oliveri & Sebastian Brusca & Ferdinando Chiacchio, 2024. "A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant," Energies, MDPI, vol. 17(7), pages 1-24, March.
    3. Mo, Yipeng & Wang, Haoxin & Yang, Chengteng & Yao, Zuhua & Li, Bixiong & Fan, Songhai & Mo, Site, 2024. "FDNet: Frequency filter enhanced dual LSTM network for wind power forecasting," Energy, Elsevier, vol. 312(C).
    4. Dong, Weichao & Sun, Hexu & Tan, Jianxin & Li, Zheng & Zhang, Jingxuan & Yang, Huifang, 2022. "Regional wind power probabilistic forecasting based on an improved kernel density estimation, regular vine copulas, and ensemble learning," Energy, Elsevier, vol. 238(PC).
    5. Katarina Pegg & Grant Wilson & Bushra Al-Duri, 2025. "Exploring Trigeneration in MSW Gasification: An Energy Recovery Potential Study Using Monte Carlo Simulation," Energies, MDPI, vol. 18(5), pages 1-23, February.
    6. Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
    7. Deng, Jiewen & Xiao, Zhao & Zhao, Qiancheng & Zhan, Jun & Tao, Jie & Liu, Minghua & Song, Dongran, 2024. "Wind turbine short-term power forecasting method based on hybrid probabilistic neural network," Energy, Elsevier, vol. 313(C).
    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. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    2. Jonkers, Jef & Avendano, Diego Nieves & Van Wallendael, Glenn & Van Hoecke, Sofie, 2024. "A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests," Applied Energy, Elsevier, vol. 361(C).
    3. Yongkang Liu & Yi Gu & Yuwei Long & Qinyu Zhang & Yonggang Zhang & Xu Zhou, 2025. "Research on Physically Constrained VMD-CNN-BiLSTM Wind Power Prediction," Sustainability, MDPI, vol. 17(3), pages 1-21, January.
    4. Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
    5. Li, Yanting & Peng, Xinghao & Zhang, Yu, 2022. "Forecasting methods for wind power scenarios of multiple wind farms based on spatio-temporal dependency structure," Renewable Energy, Elsevier, vol. 201(P1), pages 950-960.
    6. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.
    7. Tadashi Hosoe & Kazuto Yukita, 2024. "A Study on Wind Collection Effect of Vertical Axis Windmills," Energies, MDPI, vol. 17(23), pages 1-11, December.
    8. Wen, Honglin, 2024. "Probabilistic wind power forecasting resilient to missing values: An adaptive quantile regression approach," Energy, Elsevier, vol. 300(C).
    9. Bin Ji & Haiyang Huang & Yu Gao & Fangliang Zhu & Jie Gao & Chen Chen & Samson S. Yu & Zenghai Zhao, 2025. "Long-Term Stochastic Co-Scheduling of Hydro–Wind–PV Systems Using Enhanced Evolutionary Multi-Objective Optimization," Sustainability, MDPI, vol. 17(5), pages 1-34, March.
    10. Fan, Huijing & Zhen, Zhao & Liu, Nian & Sun, Yiqian & Chang, Xiqiang & Li, Yu & Wang, Fei & Mi, Zengqiang, 2023. "Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method," Energy, Elsevier, vol. 266(C).
    11. Wang, Sen & Sun, Yonghui & Zhang, Wenjie & Chung, C.Y. & Srinivasan, Dipti, 2024. "Very short-term wind power forecasting considering static data: An improved transformer model," Energy, Elsevier, vol. 312(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:gam:jeners:v:18:y:2025:i:7:p:1646-:d:1620247. 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.