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

Listed author(s):
  • Grzegorz Marcjasz
  • Bartosz Uniejewski
  • Rafal Weron

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.

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File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_17_03.pdf
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Paper provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Research Reports with number HSC/17/03.

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Length: 19 pages
Date of creation: 29 Jul 2017
Handle: RePEc:wuu:wpaper:hsc1703
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