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Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective

  • Liebl, Dominik

Classical time series models have serious difficulties in modeling and forecasting the enormous fluctuations of electricity spot prices. Markov regime switch models belong to the most often used models in the electric- ity literature. These models try to capture the fluctuations of electricity spot prices by using different regimes, each with its own mean and covariance structure. Usually one regime is dedicated to moderate prices and another is dedicated to high prices. However, these models show poor performance and there is no theoretical justification for this kind of classification. The merit or- der model, the most important micro-economic pricing model for electricity spot prices, however, suggests a continuum of mean levels with a functional dependence on electricity demand. We propose a new statistical perspective on modeling and forecasting electricity spot prices that accounts for the merit order model. In a first step, the functional relation between electricity spot prices and electricity demand is modeled by daily price-demand functions. In a second step, we parameter- ize the series of daily price-demand functions using a functional factor model. The power of this new perspective is demonstrated by a forecast study that compares our functional factor model with two established classical time se- ries models as well as two alternative functional data models.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 50881.

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Date of creation: Sep 2013
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Publication status: Published in The Annals of Applied Statistics 3.7(2013): pp. 1562-1592
Handle: RePEc:pra:mprapa:50881
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  1. Kosater, Peter & Mosler, Karl, 2005. "Can Markov-regime switching models improve power price forecasts? Evidence for German daily power prices," Discussion Papers in Econometrics and Statistics 1/05, University of Cologne, Institute of Econometrics and Statistics.
  2. Szymon Borak & Rafał Weron, 2008. "A semiparametric factor model for electricity forward curve dynamics," SFB 649 Discussion Papers SFB649DP2008-050, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. Daniel Gervini, 2008. "Robust functional estimation using the median and spherical principal components," Biometrika, Biometrika Trust, vol. 95(3), pages 587-600.
  4. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
  5. Szymon Borak & Wolfgang Härdle & Enno Mammen & Byeong U. Park, 2007. "Time Series Modelling with Semiparametric Factor Dynamics," SFB 649 Discussion Papers SFB649DP2007-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  6. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  7. Jaromir Antoch & Lubos Prchal & Maria Rosaria De Rosa & Pascal Sarda, 2010. "Electricity consumption prediction with functional linear regression using spline estimators," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2027-2041.
  8. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
  9. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
  10. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
  11. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
  12. Rafal Weron & Michael Bierbrauer & Stefan Trück, 2003. "Modeling electricity prices: jump diffusion and regime switching," HSC Research Reports HSC/03/01, Hugo Steinhaus Center, Wroclaw University of Technology.
  13. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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