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

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  • 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
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    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).

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