IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v10y1985i6p727-732.html
   My bibliography  Save this article

Forecasting electric loads with multiple predictors

Author

Listed:
  • Bunn, Derek W.

Abstract

The simultaneous use of several forecasting models is becoming increasingly common in electric load prediction. We discuss some of the concepts and methods involved in this approach, present a form of taxonomy and evaluate counterarguments. Ultimately, a pragmatic justification for the approach is adopted, recognising its surrogate nature for either more comprehensive model-building or identification. Overall, we seek to develop a critical perspective on applications of this approach in the electric power industry.

Suggested Citation

  • Bunn, Derek W., 1985. "Forecasting electric loads with multiple predictors," Energy, Elsevier, vol. 10(6), pages 727-732.
    • Handle: RePEc:eee:energy:v:10:y:1985:i:6:p:727-732
    DOI: 10.1016/0360-5442(85)90105-7
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0360544285901057
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/0360-5442(85)90105-7?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. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
    2. Elena Verdolini & Laura Díaz Anadón & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2018. "Future Prospects for Energy Technologies: Insights from Expert Elicitations," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 133-153.
    3. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    4. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.

    More about this item

    Statistics

    Access and download statistics

    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:10:y:1985:i:6:p:727-732. 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/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.