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Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models

Author

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  • Grzegorz Marcjasz
  • Bartosz Uniejewski
  • Rafal Weron

Abstract

In day-ahead electricity price forecasting the daily and weekly seasonalities are always taken into account, but the long-term seasonal component was believed to add unnecessary complexity and in most studies ignored. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, the latter is based on linear models estimated using Ordinary Least Squares. Here we show that considering non-linear neural network-type models with the same inputs as the corresponding SCAR model can lead to a yet better performance. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can significantly outperform the latter.

Suggested Citation

  • 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.
    • Handle: RePEc:wuu:wpaper:hsc1703
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Ismail Shah & Hasnain Iftikhar & Sajid Ali & Depeng Wang, 2019. "Short-Term Electricity Demand Forecasting Using Components Estimation Technique," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. 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.
    3. 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.
    4. 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.
    5. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    6. Christian Giovanelli & Seppo Sierla & Ryutaro Ichise & Valeriy Vyatkin, 2018. "Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices," Energies, MDPI, vol. 11(7), pages 1-22, July.
    7. 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.
    8. 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.
    9. Rinne, Sonja, 2018. "Radioinactive: Are nuclear power plant outages in France contagious to the German electricity price?," CIW Discussion Papers 3/2018, University of Münster, Center for Interdisciplinary Economics (CIW).
    10. Katarzyna Hubicka & Grzegorz Marcjasz & Rafal Weron, 2018. "A note on averaging day-ahead electricity price forecasts across calibration windows," HSC Research Reports HSC/18/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    11. 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.

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    More about this item

    Keywords

    Electricity spot price; Forecasting; Day-ahead market; Long-term seasonal component; Neural network; Committee machine;
    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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

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