IDEAS home Printed from https://ideas.repec.org/p/wuu/wpaper/hsc1701.html
   My bibliography  Save this paper

Variance stabilizing transformations for electricity spot price forecasting

Author

Listed:
  • Bartosz Uniejewski
  • Rafal Weron
  • Florian Ziel

Abstract

Most electricity spot price series exhibit price spikes. These extreme observations may significantly impact the obtained model estimates and hence reduce efficiency of the employed predictive algorithms. For markets with only positive prices the logarithmic transform is the single most commonly used technique to reduce spike severity and consequently stabilize the variance. However, for datasets with very close to zero (like the Spanish) or negative (like the German) prices the log-transform is not feasible. What reasonable choices do we have then? To address this issue, we conduct a comprehensive forecasting study involving 12 datasets from diverse power markets and evaluate 16 variance stabilizing transformations. We find that the probability integral transform (PIT) combined with the standard Gaussian distribution yields the best approach, significantly better than many of the considered alternatives.

Suggested Citation

  • Bartosz Uniejewski & Rafal Weron & Florian Ziel, 2017. "Variance stabilizing transformations for electricity spot price forecasting," HSC Research Reports HSC/17/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    • Handle: RePEc:wuu:wpaper:hsc1701
    as

    Download full text from publisher

    File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_17_01.pdf
    File Function: Original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    2. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    5. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    6. 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.
    7. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    8. 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.
    9. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    10. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    11. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    12. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    13. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    2. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    3. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    4. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    5. Florian Ziel & Rafal Weron, 2016. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate models," HSC Research Reports HSC/16/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    6. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    7. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    8. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    9. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    10. 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.
    11. Bartosz Uniejewski & Jakub Nowotarski & Rafał Weron, 2016. "Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 9(8), pages 1-22, August.
    12. Bartosz Uniejewski & Rafał Weron, 2018. "Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models," Energies, MDPI, vol. 11(8), pages 1-26, August.
    13. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    14. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    15. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    16. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    17. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    18. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    19. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
    20. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.

    More about this item

    Keywords

    Electricity spot price; Forecasting; Variance stabilizing transformation; Probability integral transform; Price spike; Diebold-Mariano test;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wuu:wpaper:hsc1701. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rafal Weron (email available below). General contact details of provider: https://edirc.repec.org/data/hspwrpl.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.