IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v317y2025ics036054422500218x.html
   My bibliography  Save this article

Generative probabilistic forecasting of wind power: A Denoising-Diffusion-based nonstationary signal modeling approach

Author

Listed:
  • Liu, Jingxuan
  • Zang, Haixiang
  • Cheng, Lilin
  • Ding, Tao
  • Wei, Zhinong
  • Sun, Guoqiang

Abstract

The large-scale integration of wind generation results in considerable uncertainties in power systems because of the nonstationary and stochastic nature. However, limited studies have been focused on the nonstationary properties of wind power. Also, accurate modeling the uncertainty of wind power is yet to be achieved. In this study, an integrated model with Diffusion as the backbone and nonstationary enhancement as the kernel was proposed for probabilistic wind power forecasting. First, Denoising Diffusion was established to simulate the uncertainty of the wind power series through diffusion and denoising processes. Subsequently, the transition probabilities of Diffusion in reverse process were learned by a novel nonstationary enhancement, which was designed to prevent over-stationarization and enhance temporal dependencies. As case study reveals, the proposed method can improve stability and robustness, which can fulfill the requirements of wind power probabilistic forecasting from 10 min to 2.5 h.

Suggested Citation

  • Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2025. "Generative probabilistic forecasting of wind power: A Denoising-Diffusion-based nonstationary signal modeling approach," Energy, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:energy:v:317:y:2025:i:c:s036054422500218x
    DOI: 10.1016/j.energy.2025.134576
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422500218X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134576?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

    for a different version of it.

    References listed on IDEAS

    as
    1. He, Jinhua & Hu, Zechun & Wang, Songpo & Mujeeb, Asad & Yang, Pengwei, 2024. "Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention," Energy, Elsevier, vol. 310(C).
    2. Gao, Chong & Lin, Junjie & Zeng, Jianfeng & Han, Fengwu, 2022. "Wind-photovoltaic co-generation prediction and energy scheduling of low-carbon complex regional integrated energy system with hydrogen industry chain based on copula-MILP," Applied Energy, Elsevier, vol. 328(C).
    3. Ma, Bin & Guo, Xing & Li, Penghui, 2023. "Adaptive energy management strategy based on a model predictive control with real-time tuning weight for hybrid energy storage system," Energy, Elsevier, vol. 283(C).
    4. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
    5. Zhang, Zhendong & Dai, Huichao & Jiang, Dingguo & Yu, Yi & Tian, Rui, 2024. "Multi-step ahead forecasting of wind vector for multiple wind turbines based on new deep learning model," Energy, Elsevier, vol. 304(C).
    6. Arooj, Qumrish, 2024. "FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions," Energy, Elsevier, vol. 309(C).
    7. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    8. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    9. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    10. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    11. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    12. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(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. Xing, Qianyi & Huang, Xiaojia & Wang, Kang & Wang, Jianzhou & Wang, Shuai, 2025. "MIG-EWPFS: An ensemble probabilistic wind speed forecasting system integrating multi-dimensional feature extraction, hybrid quantile regression, and Knee improved multi-objective optimization," Energy, Elsevier, vol. 324(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. Tiantian Tu, 2025. "Bridging Short- and Long-Term Dependencies: A CNN-Transformer Hybrid for Financial Time Series Forecasting," Papers 2504.19309, arXiv.org.
    2. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    3. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    4. Geng, Donghan & Zhang, Yongkang & Zhang, Yunlong & Qu, Xingchuang & Li, Longfei, 2025. "A hybrid model based on CapSA-VMD-ResNet-GRU-attention mechanism for ultra-short-term and short-term wind speed prediction," Renewable Energy, Elsevier, vol. 240(C).
    5. Zhang, Hanyu & Zandehshahvar, Reza & Tanneau, Mathieu & Van Hentenryck, Pascal, 2025. "Weather-informed probabilistic forecasting and scenario generation in power systems," Applied Energy, Elsevier, vol. 384(C).
    6. Xu, Shilin & Liu, Yang & Jin, Chun, 2023. "Forecasting daily tourism demand with multiple factors," Annals of Tourism Research, Elsevier, vol. 103(C).
    7. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    8. Hu, Lipeng & Tang, Jinjun & Xu, Fuqiao & Liang, Xiao, 2025. "SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance," Energy, Elsevier, vol. 324(C).
    9. Niu, Zhewen & Han, Xiaoqing & Zhang, Dongxia & Wu, Yuxiang & Lan, Songyan, 2024. "Interpretable wind power forecasting combining seasonal-trend representations learning with temporal fusion transformers architecture," Energy, Elsevier, vol. 306(C).
    10. Liu, Lei & Wang, Xinyu & Dong, Xue & Chen, Kang & Chen, Qiuju & Li, Bin, 2024. "Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series," Applied Energy, Elsevier, vol. 374(C).
    11. Frison, Lilli & Gölzhäuser, Simon & Bitterling, Moritz & Kramer, Wolfgang, 2024. "Evaluating different artificial neural network forecasting approaches for optimizing district heating network operation," Energy, Elsevier, vol. 307(C).
    12. Fan, Jingmin & Zhong, Mingwei & Guan, Yuanpeng & Yi, Siqi & Xu, Cancheng & Zhai, Yanpeng & Zhou, Yongwang, 2024. "An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration," Energy, Elsevier, vol. 299(C).
    13. Chen, Yuejiang & He, Yingjing & Xiao, Jiang-Wen & Wang, Yan-Wu & Li, Yuanzheng, 2024. "Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting," Energy, Elsevier, vol. 304(C).
    14. Tuominen, Jalmari & Pulkkinen, Eetu & Peltonen, Jaakko & Kanniainen, Juho & Oksala, Niku & Palomäki, Ari & Roine, Antti, 2024. "Forecasting emergency department occupancy with advanced machine learning models and multivariable input," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1410-1420.
    15. Wen, Honglin, 2024. "Probabilistic wind power forecasting resilient to missing values: An adaptive quantile regression approach," Energy, Elsevier, vol. 300(C).
    16. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
    17. Xu, Mengjie & Li, Qianwen & Zhao, Zhengtang & Sun, Chuanwang, 2024. "Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses," Energy, Elsevier, vol. 309(C).
    18. Lin, Qingcheng & Cai, Huiling & Liu, Hanwei & Li, Xuefeng & Xiao, Hui, 2024. "A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection," Energy, Elsevier, vol. 288(C).
    19. Tai, Sheng-Lun & Gaudet, Brian & Feng, Sha & Krishnamurthy, Raghavendra & Berg, Larry K. & Fast, Jerome D., 2025. "Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign," Renewable Energy, Elsevier, vol. 239(C).
    20. Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:317:y:2025:i:c:s036054422500218x. 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.