IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i20p5452-d431117.html
   My bibliography  Save this article

Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks

Author

Listed:
  • Rodrigo A. de Marcos

    (Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain)

  • Derek W. Bunn

    (Management Science and Operations, London Business School, London NW1 4SA, UK)

  • Antonio Bello

    (Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain)

  • Javier Reneses

    (Institute for Research in Technology, Technical School of Engineering (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain)

Abstract

This paper develops a new approach to short-term electricity forecasting by focusing upon the dynamic specification of an appropriate calibration dataset prior to model specification. It challenges the conventional forecasting principles which argue that adaptive methods should place most emphasis upon recent data and that regime-switching should likewise model transitions from the latest regime. The approach in this paper recognises that the most relevant dataset in the episodic, recurrent nature of electricity dynamics may not be the most recent. This methodology provides a dynamic calibration dataset approach that is based on cluster analysis applied to fundamental market regime indicators, as well as structural time series breakpoint analyses. Forecasting is based upon applying a hybrid fundamental optimisation model with a neural network to the appropriate calibration data. The results outperform other benchmark models in backtesting on data from the Iberian electricity market of 2017, which presents a considerable number of market structural breaks and evolving market price drivers.

Suggested Citation

  • Rodrigo A. de Marcos & Derek W. Bunn & Antonio Bello & Javier Reneses, 2020. "Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks," Energies, MDPI, vol. 13(20), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5452-:d:431117
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/20/5452/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/20/5452/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    2. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    3. Hong, Tao & Pinson, Pierre & Fan, Shu, 2014. "Global Energy Forecasting Competition 2012," International Journal of Forecasting, Elsevier, vol. 30(2), pages 357-363.
    4. 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.
    5. 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.
    6. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. 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.
    8. 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.
    9. Bello, Antonio & Reneses, Javier & Muñoz, Antonio & Delgadillo, Andrés, 2016. "Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques," International Journal of Forecasting, Elsevier, vol. 32(3), pages 966-980.
    10. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
    11. Antonio Bello & Javier Reneses & Antonio Muñoz, 2016. "Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case," Energies, MDPI, vol. 9(3), pages 1-27, March.
    12. Antonio Bello & Derek Bunn & Javier Reneses & Antonio Muñoz, 2016. "Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices," Energies, MDPI, vol. 9(11), pages 1-15, November.
    13. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    14. 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.
    15. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    16. 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.
    17. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez & Pedro Conde, 2016. "Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market," Energies, MDPI, vol. 9(9), pages 1-24, September.
    18. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    19. Paraschiv, Florentina & Fleten, Stein-Erik & Schürle, Michael, 2015. "A spot-forward model for electricity prices with regime shifts," Energy Economics, Elsevier, vol. 47(C), pages 142-153.
    20. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    2. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    3. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    4. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2021. "The Merit-Order Effect on the Swedish bidding zone with the highest electricity flow in the Elspot market," Energy Economics, Elsevier, vol. 102(C).
    5. Julia Nasiadka & Weronika Nitka & Rafa{l} Weron, 2022. "Calibration window selection based on change-point detection for forecasting electricity prices," Papers 2204.00872, arXiv.org.
    6. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    7. Ciarreta, Aitor & Martinez, Blanca & Nasirov, Shahriyar, 2023. "Forecasting electricity prices using bid data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1253-1271.

    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. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    2. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    8. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    9. 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.
    10. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    11. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
    12. Ziel, Florian & Steinert, Rick, 2018. "Probabilistic mid- and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 251-266.
    13. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    14. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    15. Florian Ziel & Kevin Berk, 2019. "Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules," Papers 1910.07325, arXiv.org.
    16. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    17. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    18. Tim Janke & Florian Steinke, 2019. "Forecasting the Price Distribution of Continuous Intraday Electricity Trading," Energies, MDPI, vol. 12(22), pages 1-14, November.
    19. 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.
    20. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.

    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:13:y:2020:i:20:p:5452-:d:431117. 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: 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. RePEc uses bibliographic data supplied by the respective publishers.