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ENERGIES_14_3249_PYTHON: Market data and PYTHON codes for computing electricity spot price forecasts using LASSO-estimated AR (LEAR) models as utilized in Jedrzejewski et al. (2021) Energies 14, 3249

Author

Listed:
  • Arkadiusz Jedrzejewski
  • Grzegorz Marcjasz
  • Rafal Weron

Programming Language

PYTHON

Abstract

This ZIP file contains power market data (CSV files) and PYTHON codes for computing electricity spot price forecasts using LASSO-estimated AR (LEAR) models as utilized in A. Jedrzejewski, G. Marcjasz, R. Weron (2021) Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Parameter-rich models estimated via the LASSO, Energies 14(11), 3249 (http://dx.doi.org/10.3390/en14113249).

Suggested Citation

  • Arkadiusz Jedrzejewski & Grzegorz Marcjasz & Rafal Weron, 2021. "ENERGIES_14_3249_PYTHON: Market data and PYTHON codes for computing electricity spot price forecasts using LASSO-estimated AR (LEAR) models as utilized in Jedrzejewski et al. (2021) Energies 14, 3249," WORMS Software (WORking papers in Management Science Software) WORMS/C/21/03, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    • Handle: RePEc:ahh:wcodes:wormsc2103
    as

    Download full text from publisher

    File URL: https://worms.pwr.edu.pl/RePEc/ahh/wcodes/Energies_14_3249_Python.zip
    File Function: Zipped file
    Download Restriction: no
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