IDEAS home Printed from
   My bibliography  Save this article

Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm


  • Amjady, N.
  • Keynia, F.


Short-term load forecast (STLF) is a key issue for operation of both regulated power systems and electricity markets. In spite of all performed research in this area, there is still an essential need for more accurate and robust load forecast methods. In this paper, a new hybrid forecast method is proposed for this purpose, composed of wavelet transform (WT), neural network (NN) and evolutionary algorithm (EA). Hourly load time series usually consists of both global smooth trends and sharp local variations, i.e. low- and high-frequency components. WT can efficiently decompose the time series into its components. Each component is predicted by a combination of NN and EA and then by inverse WT the hourly load forecast is obtained. The proposed method is examined on three practical power systems and compared with some of the most recent STLF methods.

Suggested Citation

  • Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
  • Handle: RePEc:eee:energy:v:34:y:2009:i:1:p:46-57
    DOI: 10.1016/

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.


    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:34:y:2009:i:1:p:46-57. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.