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

Author

Listed:
  • 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
    as

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    References listed on IDEAS

    as
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    Keywords

    Electricity spot price; Forecasting; Day-ahead market; Long-term seasonal component; Neural network; Committee machine;

    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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