Advertisement

Modelling and Forecasting Energy Demand: Principles and Difficulties

  • Martin Fischer
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

Abstract

We give a brief description of how energy demand can be modelled as a function of calendar data, meteorological data and economic variables. The principles of energy demand models are presented and a brief overview of commonly used mathematical methods is given. For each method advantages and disadvantages are described. Some examples illustrate difficulties that may be encountered when using meteorological data in energy models. These difficulties are discussed and we propose some steps weather services could take to facilitate the use of meteorological data in the energy sector.

Keywords

Energy demand forecast modeling non-linear 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dutton JA. 2009: Chapter 1, This volume.Google Scholar
  2. ECMWF home page 23 March 2009: www.ecmwf.int/research/demeter
  3. Friedman J.H. 1991: Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19.Google Scholar
  4. Hastie T., Tibshirani R., and Friedman J.H. 2001: The Elements of Statistical Learning, Springer, New York, ISBN 978-0387952840.Google Scholar
  5. Smola A.J. and Schoelkopf B. 1998: A Tutorial on Support Vector Regression.Google Scholar
  6. NeuroCOLT2 Technical Report Series, NC2-TR-1998-030, www.neurocolt.com
  7. Smola A.J. and Schoelkopf B. 2003: A Tutorial on Support Vector Regression, http://eprints.pascal-network.org/archive/00002057/01/SmoSch03b.pdf
  8. Tay F.E.H. and Cao L. 2001: Application of support vector machines in financial time series forecasting. Omega, 29, 309–317.CrossRefGoogle Scholar
  9. Tendances Carbon 2009: No 33, February 2009. The Monthly Bulletin on the European Carbon Market. In collaboration between Caisse des Dépôts-Mission Climat and Bluenext SA. May.armstrong@cassedesdepots.fr, c.dufour@bluenext.euGoogle Scholar
  10. Vapnik V.N. 1995: The Nature of Statistical Learning Theory, Springer, New York.Google Scholar
  11. Vapnik V.N., Golowich S.E., and Smola A.J. 1996: Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Martin Fischer

There are no affiliations available

Personalised recommendations