IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v22y2006i2p113-125.html
   My bibliography  Save this article

The seasonal forecast of electricity demand: a hierarchical Bayesian model with climatological weather generator

Author

Listed:
  • Sergio Pezzulli
  • Patrizio Frederic
  • Shanti Majithia
  • Sal Sabbagh
  • Emily Black
  • Rowan Sutton
  • David Stephenson

Abstract

In this paper we focus on the one year ahead prediction of the electricity peak‐demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • Sergio Pezzulli & Patrizio Frederic & Shanti Majithia & Sal Sabbagh & Emily Black & Rowan Sutton & David Stephenson, 2006. "The seasonal forecast of electricity demand: a hierarchical Bayesian model with climatological weather generator," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(2), pages 113-125, March.
    • Handle: RePEc:wly:apsmbi:v:22:y:2006:i:2:p:113-125
    DOI: 10.1002/asmb.622
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.622
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.622?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
    ---><---

    Citations

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


    Cited by:

    1. Benedetto Grillone & Gerard Mor & Stoyan Danov & Jordi Cipriano & Florencia Lazzari & Andreas Sumper, 2021. "Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology," Energies, MDPI, vol. 14(17), pages 1-30, September.
    2. Fonseca, Jimeno A. & Nevat, Ido & Peters, Gareth W., 2020. "Quantifying the uncertain effects of climate change on building energy consumption across the United States," Applied Energy, Elsevier, vol. 277(C).
    3. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.

    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:wly:apsmbi:v:22:y:2006:i:2:p:113-125. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.