IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v266y2023ics0360544222033060.html
   My bibliography  Save this article

Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method

Author

Listed:
  • Fan, Huijing
  • Zhen, Zhao
  • Liu, Nian
  • Sun, Yiqian
  • Chang, Xiqiang
  • Li, Yu
  • Wang, Fei
  • Mi, Zengqiang

Abstract

-Probabilistic wind power forecasting includes more detailed information than deterministic forecasting, which can provide reliable guidance for the optimal decisions of power system scheduling operation. However, there are certain laws in the magnitude and direction of the forecasting errors corresponding to different power series fluctuations, which leads to different predictability and forecasting accuracy of different power fluctuation patterns. As most studies still focused on the model algorithm improvement and pay less attention to the law of power data itself, this paper proposes a novel probabilistic forecasting method based on the swinging door algorithm (SDA), fuzzy c means (FCM) clustering method, long short-term memory (LSTM) neural network, and nonparametric kernel density estimation (KDE), considering the correlation between wind power fluctuation patterns and forecasting errors. SDA and FCM are used to assign appropriate pattern labels to the power fluctuations, and then LSTM and KDE are used to introduce pattern recognition results in probabilistic forecasting models, excavating the inherent law of the data for classification modeling. Simulation shows that the proposed model can adapt to different error distribution patterns, and the models introduced fluctuation pattern recognition can improve the skill score of probabilistic forecasting by 36.50% on average than those without pattern recognition.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033060
    DOI: 10.1016/j.energy.2022.126420
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222033060
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126420?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. Mahmoud, Tawfek & Dong, Z.Y. & Ma, Jin, 2018. "An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine," Renewable Energy, Elsevier, vol. 126(C), pages 254-269.
    2. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
    3. Wang, Fei & Chen, Peng & Zhen, Zhao & Yin, Rui & Cao, Chunmei & Zhang, Yagang & Duić, Neven, 2022. "Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method," Applied Energy, Elsevier, vol. 323(C).
    4. 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.
    5. 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).
    6. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    7. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    8. Hu, Jianming & Tang, Jingwei & Lin, Yingying, 2020. "A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization," Renewable Energy, Elsevier, vol. 149(C), pages 141-164.
    9. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    10. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
    11. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
    12. 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).
    13. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
    14. 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).
    15. Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
    16. Fei Wang & Liming Liu & Yili Yu & Gang Li & Jessica Li & Miadreza Shafie-khah & João P. S. Catalão, 2018. "Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response," Energies, MDPI, vol. 11(4), pages 1-22, March.
    17. Cui, Mingjian & Zhang, Jie & Feng, Cong & Florita, Anthony R. & Sun, Yuanzhang & Hodge, Bri-Mathias, 2017. "Characterizing and analyzing ramping events in wind power, solar power, load, and netload," Renewable Energy, Elsevier, vol. 111(C), pages 227-244.
    18. Wang, Jianzhou & Hu, Jianming, 2015. "A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vec," Energy, Elsevier, vol. 93(P1), pages 41-56.
    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. 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. Yun, Eunjeong & Hur, Jin, 2021. "Probabilistic estimation model of power curve to enhance power output forecasting of wind generating resources," Energy, Elsevier, vol. 223(C).
    3. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    4. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    5. 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.
    6. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
    8. Liang, Tao & Chai, Chunjie & Sun, Hexu & Tan, Jianxin, 2022. "Wind speed prediction based on multi-variable Capsnet-BILSTM-MOHHO for WPCCC," Energy, Elsevier, vol. 250(C).
    9. Liu, Xin & Yang, Luoxiao & Zhang, Zijun, 2022. "The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions," Applied Energy, Elsevier, vol. 324(C).
    10. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Moreno, Sinvaldo Rodrigues & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2021. "A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting," Energy, Elsevier, vol. 216(C).
    11. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    12. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    13. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    14. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
    15. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    16. Xu, Xuefang & Hu, Shiting & Shao, Huaishuang & Shi, Peiming & Li, Ruixiong & Li, Deguang, 2023. "A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm," Energy, Elsevier, vol. 284(C).
    17. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
    18. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
    19. Zhang, Jincheng & Zhao, Xiaowei, 2021. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements," Applied Energy, Elsevier, vol. 288(C).
    20. Lv, Jiaqing & Zheng, Xiaodong & Pawlak, Mirosław & Mo, Weike & Miśkowicz, Marek, 2021. "Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms," Renewable Energy, Elsevier, vol. 177(C), pages 181-192.

    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:266:y:2023:i:c:s0360544222033060. 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.