IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i8p2039-d162196.html
   My bibliography  Save this article

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

Author

Listed:
  • Bartosz Uniejewski

    (Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
    Faculty of Pure and Applied Mathematics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Rafał Weron

    (Department of Operations Research, Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

Abstract

Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline ) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2039-:d:162196
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/8/2039/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/8/2039/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    2. 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.
    3. Lars Ivar Hagfors & Hilde Hørthe Kamperud & Florentina Paraschiv & Marcel Prokopczuk & Alma Sator & Sjur Westgaard, 2016. "Prediction of extreme price occurrences in the German day-ahead electricity market," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1929-1948, December.
    4. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    5. 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 Technology.
    6. Adam Misiorek, 2008. "Short-term forecasting of electricity prices: Do we need a different model for each hour?," HSC Research Reports HSC/08/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. 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.
    8. Maciejowska, Katarzyna & Nowotarski, Jakub, 2016. "A hybrid model for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1051-1056.
    9. Sergey Voronin & Jarmo Partanen, 2013. "Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks," Energies, MDPI, vol. 6(11), pages 1-24, November.
    10. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    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. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. 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.
    14. 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.
    15. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    16. Gaillard, Pierre & Goude, Yannig & Nedellec, Raphaël, 2016. "Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1038-1050.
    17. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    18. 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.
    19. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    25. Adam Misiorek & Rafal Weron, 2006. "Interval forecasting of spot electricity prices," HSC Research Reports HSC/06/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    26. 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.
    27. 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 Technology.
    28. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    29. Anna Kowalska-Pyzalska, 2018. "An Empirical Analysis of Green Electricity Adoption Among Residential Consumers in Poland," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    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. 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.
    2. 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.
    3. 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.
    4. 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 Technology.
    5. 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.
    6. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. 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.
    8. 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.
    9. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    10. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    11. 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.
    12. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    13. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    14. 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.
    15. 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 Technology.
    16. 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.
    17. Arkadiusz Jędrzejewski & Grzegorz Marcjasz & Rafał Weron, 2021. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO," Energies, MDPI, vol. 14(11), pages 1-17, June.
    18. 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.
    19. 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.
    20. 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.

    More about this item

    Keywords

    electricity spot price; day-ahead market; long-term seasonal component; LASSO; automated variable selection; variance stabilizing transformation;
    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

    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:gam:jeners:v:11:y:2018:i:8:p:2039-:d:162196. 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: . General contact details of provider: https://www.mdpi.com .

    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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.