IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v218y2023ics0960148123012727.html
   My bibliography  Save this article

Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method

Author

Listed:
  • Karijadi, Irene
  • Chou, Shuo-Yan
  • Dewabharata, Anindhita

Abstract

A precise wind power forecast is required for the renewable energy platform to function effectively. By having a precise wind power forecast, the power system can better manage its supply and ensure grid reliability. However, the nature of wind power generation is intermittent and exhibits high randomness, which poses a challenge to obtaining accurate forecasting results. In this study, a hybrid method is proposed based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Empirical Wavelet Transform (EWT), and deep learning-based Long Short-Term Memory (LSTM) for ultra-short-term wind power forecasting. A combination of CEEMDAN and EWT is used as the preprocessing technique, where CEEMDAN is first employed to decompose the original wind power data into several subseries, and the EWT denoising technique is used to denoise the highest frequency series generated from CEEMDAN. Then, LSTM is utilized to forecast all the subseries from the CEEMDAN-EWT process, and the forecasting results of each subseries are aggregated to achieve the final forecasting results. The proposed method is validated on real-world wind power data in France and Turkey. Our experimental results demonstrate that the proposed method can forecast more accurately than the benchmarking methods.

Suggested Citation

  • Karijadi, Irene & Chou, Shuo-Yan & Dewabharata, Anindhita, 2023. "Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method," Renewable Energy, Elsevier, vol. 218(C).
  • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123012727
    DOI: 10.1016/j.renene.2023.119357
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123012727
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.119357?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.

    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:renene:v:218:y:2023:i:c:s0960148123012727. 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.journals.elsevier.com/renewable-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.