IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v255y2019ics030626191931520x.html
   My bibliography  Save this article

Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method

Author

Listed:
  • Zhao, Jing
  • Wang, Jianzhou
  • Guo, Zhenhai
  • Guo, Yanling
  • Lin, Wantao
  • Lin, Yihua

Abstract

At present, a single-valued deterministic simulation method is preferred choice for numerical wind speed forecasts. However, it remains difficult to meet the actual needs of both wind farms and grid systems, mainly owing to unavoidable uncertainties. The development of skilled numerical forecasting methods has become a critical issue and major challenge, and new capabilities and strategies for mitigating uncertainties in wind data derived from numerical models are highly sought after. On this topic, our study develops an improved ensemble method for day-ahead forecast of local wind speeds. The proposed method constructs an optimized system based on ensemble simulations of weather research and forecasting model, a Markov stochastic process, and an improved induced ordered weighted average approach that combines gray relationships with an evolutionary algorithm. The original contributions are concluded as: (i) using a Markov stochastic process, the observed information can be transferred to the ensemble system, which contributes to the accuracy improvement; and (ii) the optimized induced ordered weighted average model, with a member selection process, is a new data-driven ensemble method for numerical wind speed forecasting. Simulation indicates that the proposed method effectively reduces the uncertainties of numerical simulations, and performs better than other models. The simulation also shows that an ensemble with fewer members may generate better results than using a combination of all single members. This study is of great significance for both theoretical research and real applications for numerical wind speed forecasts at local sites.

Suggested Citation

  • Zhao, Jing & Wang, Jianzhou & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Lin, Yihua, 2019. "Multi-step wind speed forecasting based on numerical simulations and an optimized stochastic ensemble method," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s030626191931520x
    DOI: 10.1016/j.apenergy.2019.113833
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191931520X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113833?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yi & Von Krannichfeldt, Leandro & Zufferey, Thierry & Toubeau, Jean-François, 2021. "Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach," Applied Energy, Elsevier, vol. 304(C).
    2. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    3. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    4. 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).
    5. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    6. Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    7. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    8. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Li, Wenzhe & Li, Fei & Lee, Jay, 2021. "A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 178(C), pages 709-719.
    9. Han, Yan & Mi, Lihua & Shen, Lian & Cai, C.S. & Liu, Yuchen & Li, Kai & Xu, Guoji, 2022. "A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting," Applied Energy, Elsevier, vol. 312(C).
    10. Liu, Hui & Duan, Zhu, 2020. "A vanishing moment ensemble model for wind speed multi-step prediction with multi-objective base model selection," Applied Energy, Elsevier, vol. 261(C).
    11. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    12. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    13. Nie, Ying & Liang, Ni & Wang, Jianzhou, 2021. "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, Elsevier, vol. 301(C).
    14. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(C).
    15. 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.

    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:appene:v:255:y:2019:i:c:s030626191931520x. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.