Accessibility navigation


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

Pezzulli, S., Frederic, P., Majithia, S., Sabbagh, S., Black, E., Sutton, R. and Stephenson, D. (2006) The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator. Applied Stochastic Models in Business and Industry, 22 (2). pp. 113-125. ISSN 1524-1904

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/asmb.622

Abstract/Summary

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 (C) 2006 John Wiley & Sons, Ltd.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences
ID Code:10216
Uncontrolled Keywords:electricity load, mixed model, hierarchical Bayesian model, weather, generator, seasonal forecast, LOAD

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation