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The seasonal forecast of electricity demand: a hierarchical Bayesian model with climatological weather generator
Author(s) -
Pezzulli Sergio,
Frederic Patrizio,
Majithia Shanti,
Sabbagh Sal,
Black Emily,
Sutton Rowan,
Stephenson David
Publication year - 2006
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.622
Subject(s) - bayesian probability , flexibility (engineering) , meteorology , electricity demand , computer science , climatology , north american mesoscale model , environmental science , econometrics , numerical weather prediction , electricity generation , power (physics) , statistics , mathematics , global forecast system , artificial intelligence , geography , physics , quantum mechanics , geology
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.