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Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective

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  • Liebl, Dominik

Abstract

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.

Suggested Citation

  • Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:50881
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    File URL: https://mpra.ub.uni-muenchen.de/50881/1/MPRA_paper_50881.pdf
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    References listed on IDEAS

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    Cited by:

    1. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    2. repec:dui:wpaper:1502 is not listed on IDEAS
    3. repec:eee:econom:v:201:y:2017:i:2:p:269-291 is not listed on IDEAS
    4. Hron, K. & Menafoglio, A. & Templ, M. & Hrůzová, K. & Filzmoser, P., 2016. "Simplicial principal component analysis for density functions in Bayes spaces," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 330-350.
    5. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
    6. Daniel Kosiorowski & Jerzy P. Rydlewski & Ma{l}gorzata Snarska, 2016. "Detecting a Structural Change in Functional Time Series Using Local Wilcoxon Statistic," Papers 1604.03776, arXiv.org, revised Nov 2016.
    7. Danielle Devogelaer & Benoît Laine, 2016. "Working Paper 09-16 - Drivers of wholesale electricity prices in a small, open economy - Some evidence from the nuclear restart in Belgium
      [Working Paper 09-16 - Marché de l’électricité : facteurs
      ," Working Papers 1609, Federal Planning Bureau, Belgium.
    8. repec:eee:jmvana:v:163:y:2018:i:c:p:15-36 is not listed on IDEAS
    9. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org, revised Oct 2014.
    10. Pawel Maryniak & Stefan Trueck & Rafal Weron, 2016. "Carbon pricing, forward risk premiums and pass-through rates in Australian electricity futures markets," HSC Research Reports HSC/16/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    11. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    12. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    14. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    15. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    16. Cerovecki, Clément & Hörmann, Siegfried, 2017. "On the CLT for discrete Fourier transforms of functional time series," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 282-295.
    17. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Energy Economics, Elsevier, vol. 51(C), pages 430-444.
    18. Kokoszka, Piotr & Reimherr, Matthew & Wölfing, Nikolas, 2016. "A randomness test for functional panels," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 37-53.

    More about this item

    Keywords

    Functional factor model; functional data analysis; time series analysis; fundamental market model; merit order curve; European Energy Exchange; EEX;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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