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Automated variable selection and shrinkage for day-ahead electricity price forecasting

Listed author(s):
  • Bartosz Uniejewski
  • Jakub Nowotarski
  • Rafal Weron

In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an extensive empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets) we show that using the latter two classes can bring significant accuracy gains compared to commonly used EPF models. In particular, one of the elastic nets - a class that has not been considered in EPF before - stands out as the best performing model overall.

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File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_16_06.pdf
File Function: Final version
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Paper provided by Hugo Steinhaus Center, Wroclaw University of Technology in its series HSC Research Reports with number HSC/16/06.

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Length: 24 pages
Date of creation: 05 Jul 2016
Publication status: Published in Energies 2016, vol. 9(8), 621; doi:10.3390/en9080621
Handle: RePEc:wuu:wpaper:hsc1606
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  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.
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