IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting

  • Mestekemper, Thomas
  • Kauermann, Göran
  • Smith, Michael S.

We suggest a new approach for forecasting energy demand at an intraday resolution. The demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary time series models, with a dimension equal to the number of intraday periods. These are a periodic autoregression and a dynamic factor model. We show the benefits of our approach in the forecasting of (a) district heating demand in a steam network in Germany and (b) aggregate electricity demand in the state of Victoria, Australia. In both studies, accounting for weather can improve the forecast quality substantially, as does the use of time series models. We compare the effectiveness of the periodic autoregression with three variations of the dynamic factor model, and find that the dynamic factor model consistently provides more accurate forecasts. Overall, our approach combines many of the features which have previously been shown to provide high quality forecasts of energy demand over horizons of up to one week, as well as introducing some novel ones.

If you experience problems downloading a file, check if you have the proper application to view it first. 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://www.sciencedirect.com/science/article/pii/S0169207012000520
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 29 (2013)
Issue (Month): 1 ()
Pages: 1-12

as
in new window

Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:1-12
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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.:

as in new window
  1. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
  2. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian identification, selection and estimation of semiparametric functions in high-dimensional additive models," Journal of Econometrics, Elsevier, vol. 143(2), pages 291-316, April.
  3. Ewa Broszkiewicz-Suwaj & Andrzej Makagon & Rafal Weron & Agnieszka Wylomanska, 2005. "On detecting and modeling periodic correlation in financial data," Econometrics 0502006, EconWPA.
  4. Rotger, G.P. & Franses, Ph.H.B.F., 2006. "Forecasting high-frequency electricity demand with a diffusion index model," Econometric Institute Research Papers EI 2006-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  5. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
  6. Moral-Carcedo, Julian & Vicens-Otero, Jose, 2005. "Modelling the non-linear response of Spanish electricity demand to temperature variations," Energy Economics, Elsevier, vol. 27(3), pages 477-494, May.
  7. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  8. Sailor, David J. & Muñoz, J.Ricardo, 1997. "Sensitivity of electricity and natural gas consumption to climate in the U.S.A.—Methodology and results for eight states," Energy, Elsevier, vol. 22(10), pages 987-998.
  9. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
  10. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
  11. Jeng-Min Chiou & Hans-Georg Müller & Jane-Ling Wang, 2003. "Functional quasi-likelihood regression models with smooth random effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 405-423.
  12. repec:cup:cbooks:9780521785167 is not listed on IDEAS
  13. Göran Kauermann & Tatyana Krivobokova & Ludwig Fahrmeir, 2009. "Some asymptotic results on generalized penalized spline smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 487-503.
  14. Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
  15. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
  16. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
  17. repec:cup:cbooks:9780521780506 is not listed on IDEAS
  18. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
  19. 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.
  20. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
  21. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
  22. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  23. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
  24. 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.
  25. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer, vol. 90(1), pages 27-42, March.
  26. Simon N. Wood, 2008. "Fast stable direct fitting and smoothness selection for generalized additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 495-518.
  27. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  28. 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.
  29. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
  30. Krivobokova, Tatyana & Kauermann, Goran, 2007. "A Note on Penalized Spline Smoothing With Correlated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1328-1337, December.
  31. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:1-12. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.