This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Forecasting from one day to one week ahead for the Spanish system operator

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Jose Ramon Cancelo ()
Antoni Espasa ()
Rosemarie Grafe ()
Abstract

This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of forecasts of special interest to REE, several days ahead predictions for daily data and one day ahead hourly forecasts. Accordingly, forecast accuracy is assessed in terms of their errors. For doing so we analyze historical, real time forecasting errors for daily and hourly data for the year 2006, and report forecasting performance by day of the week, time of the year and type of day. Other aspects of the prediction problem, like the influence of the errors in predicting temperature on forecasting the load several days ahead, or the need for an adequate treatment of special days, are also investigated.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://e-archivo.uc3m.es/dspace/bitstream/10016/1146/1/ws078418.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws078418.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: Dec 2007
Date of revision:
Handle: RePEc:cte:wsrepe:ws078418

Contact details of provider:
Postal: C/ Madrid, 126 - 28903 GETAFE (MADRID)
Phone: 6249847
Fax: 6249849
Web page: http://www.uc3m.es/uc3m/dpto/DEE/departamento.html
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: ().

Related research
Keywords: Energy forecasting; Hourly and daily models; Time series; Forecasting practice;

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Smith, Michael, 2000. "Modeling and Short-term Forecasting of New South Wales Electricity System Load," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 465-78, October.
  2. Robert Engle & Clive Granger & Ramu Ramanathan & Farshid Vahid-Araghi & Casey Brace, 1992. "Short-Run Forecasts of Electricity Loads and Peaks," University of California at San Diego, Economics Working Paper Series 92-49, Department of Economics, UC San Diego.
    Other versions:
  3. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16. [Downloadable!] (restricted)
  4. Henley, Andrew & Peirson, John, 1997. "Non-linearities in Electricity Demand and Temperature: Parametric versus Non-parametric Methods," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(1), pages 149-62, February.
  5. Schultz, Randall L., 1992. "Fundamental aspects of forecasting in organizations," International Journal of Forecasting, Elsevier, vol. 7(4), pages 409-411, March. [Downloadable!] (restricted)
  6. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January. [Downloadable!] (restricted)
  7. Hippert, H.S. & Bunn, D.W. & Souza, R.C., 2005. "Large neural networks for electricity load forecasting: Are they overfitted?," International Journal of Forecasting, Elsevier, vol. 21(3), pages 425-434. [Downloadable!] (restricted)
  8. Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January. [Downloadable!] (restricted)
  9. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28. [Downloadable!] (restricted)
    Other versions:
Full references

Statistics
Access and download statistics

Did you know? Each page is provided with a technical contact, in case something is not right with the supplied information. See under "publisher info".

This page was last updated on 2009-12-15.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.