IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i3p652-d1580755.html
   My bibliography  Save this article

Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features

Author

Listed:
  • Gang Li

    (Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Chen Lin

    (Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

  • Yupeng Li

    (Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China)

Abstract

Accurate prediction of regional wind power generation intervals is an effective support tool for the economic and stable operation of provincial power grid. However, it involves a large amount of high-dimensional meteorological and historical power generation information related to massive wind power stations in a province. In this paper, a lightweight model is developed to directly obtain probabilistic predictions in the form of intervals. Firstly, the input features are formed through a fused image generation method of geographic and meteorological information as well as a power aggregation strategy, which avoids the extensive and tedious data processing process prior to modeling in the traditional approach. Then, in order to effectively consider the spatial meteorological distribution characteristics of regional power stations and the temporal characteristics of historical power, a parallel prediction network architecture of a convolutional neural network (CNN) and long short-term memory (LSTM) is designed. Meanwhile, an efficient channel attention (ECA) mechanism and an improved quantile regression-based loss function are introduced in the training to directly generate prediction intervals. The case study shows that the model proposed in this paper improves the interval prediction performance by at least 12.3% and reduces the deterministic prediction root mean square error (RMSE) by at least 19.4% relative to the benchmark model.

Suggested Citation

  • Gang Li & Chen Lin & Yupeng Li, 2025. "Probabilistic Forecasting of Provincial Regional Wind Power Considering Spatio-Temporal Features," Energies, MDPI, vol. 18(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:652-:d:1580755
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/3/652/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/3/652/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Juntao & Cheng, Chuntian & Yu, Shen, 2024. "Recognizing the mapping relationship between wind power output and meteorological information at a province level by coupling GIS and CNN technologies," Applied Energy, Elsevier, vol. 360(C).
    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. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
    4. Lu, Peng & Ye, Lin & Pei, Ming & Zhao, Yongning & Dai, Binhua & Li, Zhuo, 2022. "Short-term wind power forecasting based on meteorological feature extraction and optimization strategy," Renewable Energy, Elsevier, vol. 184(C), pages 642-661.
    5. de Azevedo Takara, Lucas & Teixeira, Ana Clara & Yazdanpanah, Hamed & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2024. "Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning," Applied Energy, Elsevier, vol. 369(C).
    6. Gong, Mingju & Yan, Changcheng & Xu, Wei & Zhao, Zhixuan & Li, Wenxiang & Liu, Yan & Li, Sheng, 2023. "Short-term wind power forecasting model based on temporal convolutional network and Informer," Energy, Elsevier, vol. 283(C).
    7. Baggio, Roberta & Muzy, Jean-François, 2024. "Improving probabilistic wind speed forecasting using M-Rice distribution and spatial data integration," Applied Energy, Elsevier, vol. 360(C).
    8. Tahmasebifar, Reza & Moghaddam, Mohsen Parsa & Sheikh-El-Eslami, Mohammad Kazem & Kheirollahi, Reza, 2020. "A new hybrid model for point and probabilistic forecasting of wind power," Energy, Elsevier, vol. 211(C).
    9. Xiaoshuang Huang & Yinbao Zhang & Jianzhong Liu & Xinjia Zhang & Sicong Liu, 2023. "A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit," Sustainability, MDPI, vol. 15(19), pages 1-13, September.
    10. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
    11. Yang, Mao & Che, Runqi & Yu, Xinnan & Su, Xin, 2024. "Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction," Energy, Elsevier, vol. 302(C).
    12. Petersen, Claire & Reguant, Mar & Segura, Lola, 2024. "Measuring the impact of wind power and intermittency," Energy Economics, Elsevier, vol. 129(C).
    13. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(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. 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).
    2. Yang, Mao & Guo, Yunfeng & Huang, Tao & Fan, Fulin & Ma, Chenglian & Fang, Guozhong, 2024. "Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods," Energy, Elsevier, vol. 312(C).
    3. Chen, Juntao & Fu, Xueying & Zhang, Lingli & Shen, Haoye & Wu, Jibo, 2024. "A novel offshore wind power prediction model based on TCN-DANet-sparse transformer and considering spatio-temporal coupling in multiple wind farms," Energy, Elsevier, vol. 308(C).
    4. Xiaoxun, Zhu & Zixu, Xu & Yu, Wang & Xiaoxia, Gao & Xinyu, Hang & Hongkun, Lu & Ruizhang, Liu & Yao, Chen & Huaxin, Liu, 2023. "Research on wind speed behavior prediction method based on multi-feature and multi-scale integrated learning," Energy, Elsevier, vol. 263(PA).
    5. 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).
    6. Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
    7. 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).
    8. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
    9. Qi, Chu & Zeng, Xianglong & Wang, Yongjian & Li, Hongguang, 2022. "Adaptive time window convolutional neural networks concerning multiple operation modes with applications in energy efficiency predictions," Energy, Elsevier, vol. 240(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. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    12. Yao, Xianshuang & Guo, Kangshuai & Lei, Jianqi & Li, Xuanyu, 2024. "Fully connected multi-reservoir echo state networks for wind power prediction," Energy, Elsevier, vol. 312(C).
    13. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    14. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    15. Li, Jianfang & Jia, Li & Zhou, Chengyu, 2024. "Probability density function based adaptive ensemble learning with global convergence for wind power prediction," Energy, Elsevier, vol. 312(C).
    16. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    17. Wei Du & Shi-Tao Peng & Pei-Sen Wu & Ming-Lang Tseng, 2024. "High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine," Energies, MDPI, vol. 17(10), pages 1-21, May.
    18. Lv, Yunlong & Hu, Qin & Xu, Hang & Lin, Huiyao & Wu, Yufan, 2024. "An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model," Energy, Elsevier, vol. 293(C).
    19. 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).
    20. Guanghui Che & Daocheng Zhou & Rui Wang & Lei Zhou & Hongfu Zhang & Sheng Yu, 2024. "Wind Energy Assessment in Forested Regions Based on the Combination of WRF and LSTM-Attention Models," Sustainability, MDPI, vol. 16(2), pages 1-17, January.

    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:3:p:652-:d:1580755. 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.