NCN 2015/17/B/HS4/00334

Probabilistic forecasting of electricity prices and demand for risk management purposes

Grant no.: NCN 2015/17/B/HS4/00334
Funding agency: National Science Centre (NCN), Poland
Funding scheme: OPUS
Funding period: 15.02.2016-14.02.2019 (36 months), extended to 14.10.2019 (44 months)
Budget: 398 400 PLN
Title in Polish: Probabilistyczne prognozowanie cen i zapotrzebowania na energię elektryczną na potrzeby zarządzania ryzykiem

Research team:

Principal Investigator (Kierownik):

Investigators (Wykonawcy):

Former investigators (Byli wykonawcy):

Collaborators (Współpracownicy):

– Ph.D. / M.Sc. / B.Sc. student

Aims and scope:

In a recent review article published in the International Journal of Forecasting, Weron (2014) identifies five challenges outstanding in the area of electricity price forecasting. The main objective of the project is to adequately address three of them:

  • the importance of considering fundamental price drivers and of an appropriate choice of the input variables in electricity spot (or forward) price models,
  • the need for developing techniques for probabilistic (i.e. interval or density) forecasting of electricity prices and demand, both for power exchange bidding and risk management purposes,
  • the need for a universal test ground involving the same datasets and the same robust error evaluation procedures.

The project will be carried out within three tasks, which are interesting both from a basic research (development and validation of econometric techniques) and a utilitarian point of view (improving portfolio management and risk management practices in the energy sector).

Tasks:

  1. Development of robust variance stabilizing transformations and seasonal decomposition techniques for preprocessing electricity prices and demand.
  2. Development of new techniques for probabilistic forecasting of electricity prices and demand, in particular using sister models and expert forecasts.
  3. Development and validation of error measures for probabilistic forecasts tailored for the extremely volatile and seasonal electricity spot prices.

Publications:

Peer-reviewed articles in JCR-listed journals

2020 (1), 2019 (6), 2018 (5), 2017 (0), 2016 (3)

  1. G. Marcjasz, B. Uniejewski, R. Weron (2020) Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?, International Journal of Forecasting 36(2), 466-479 (doi: 10.1016/j.ijforecast.2019.07.002). Working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1805.html
  2. K. Hubicka, G. Marcjasz, R. Weron (2019) A note on averaging day-ahead electricity price forecasts across calibration windows, IEEE Transactions on Sustainable Energy 10(1), 321-323 (doi: 10.1109/TSTE.2018.2869557). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1803.html
  3. K. Maciejowska, W. Nitka, T. Weron (2019) Day-ahead vs. intraday – Forecasting the price spread to maximize economic benefits, Energies 12(4), 631  (doi: 10.3390/en12040631)
  4. G. Marcjasz, B. Uniejewski, R. Weron (2019) On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks, International Journal of Forecasting 35(4), 1520-1532 (https://doi.org/10.1016/j.ijforecast.2017.11.009). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1703.html
  5. P. Maryniak, S. Trück, R. Weron (2019) Carbon pricing and electricity markets – The case of the Australian Clean Energy Bill, Energy Economics 79, 45-58 (doi: 10.1016/j.eneco.2018.06.003). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1610.html
  6. T. Serafin, B. Uniejewski, R. Weron (2019) Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting, Energies 12(13), 2561  (doi: 10.3390/en12132561)
  7. B. Uniejewski, G. Marcjasz, R. Weron (2019) On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting, Energy Economics 79, 171-182 (doi: 10.1016/j.eneco.2018.02.007). Working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1702.html
  8. G. Marcjasz, T. Serafin, R. Weron (2018) Selection of calibration windows for day-ahead electricity price forecasting, Energies 11(9), 2364 (doi: 10.3390/en11092364). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1806.html
  9. J. Nowotarski, R. Weron (2018) Recent advances in electricity price forecasting: A review of probabilistic forecasting, Renewable and Sustainable Energy Reviews 81(1), 1548-1568 (doi: 10.1016/j.rser.2017.05.234). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1607.html
  10. B. Uniejewski, R. Weron (2018) Efficient forecasting of electricity spot prices with expert and LASSO models, Energies 11(8), 2039  (doi: 10.3390/en11082039)
  11. B. Uniejewski, R. Weron, F. Ziel (2018) Variance stabilizing transformations for electricity spot price forecasting, IEEE Transactions on Power Systems 33(2), 2219-2229 (doi: 10.1109/TPWRS.2017.2734563). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1701.html
  12. F. Ziel, R. Weron (2018) Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks, Energy Economics 70, 396-420  (doi: 10.1016/j.eneco.2017.12.016). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1608.html
  13. J. Nowotarski, R. Weron (2016) On the importance of the long-term seasonal component in day-ahead electricity price forecasting, Energy Economics 57, 228-235 (doi: 10.1016/j.eneco.2016.05.009). Working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1605.html.  Matlab codes and data available from RePEc: https://ideas.repec.org/c/wuu/hscode/z16002.html
  14. S. Trück, R. Weron (2016) Convenience yields and risk premiums in the EU-ETS – Evidence from the Kyoto commitment period, Journal of Futures Markets 36(6), 587-611 (doi: 10.1002/fut.21780). Earlier working paper version available from RePEc: http://ideas.repec.org/p/wuu/wpaper/hsc1503.html
  15. B. Uniejewski, J. Nowotarski, R. Weron (2016) Automated variable selection and shrinkage for day-ahead electricity price forecasting, Energies 9(8), 621  (doi: 10.3390/en9080621). Working paper version available from RePEc: http://ideas.repec.org/p/wuu/wpaper/hsc1606.html

Peer-reviewed articles in non JCR-listed journals

  1. J. Nowotarski, R. Weron (2016) To combine or not to combine? Recent trends in electricity price forecasting, ARGO 9, 7-14.  Available from ARGO Website. Working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1601.html

Book chapters

  1. P. Maryniak, R. Weron (2020) What is the probability of an electricity price spike? Evidence from the UK power market, in “Handbook of Energy Finance: Theories, Practices and Simulations”, eds. S. Goutte, D.K. Nguyen, World Scientific, 231-245 (doi: 10.1142/11213). Earlier working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1411.html
  2. K. Maciejowska, R. Weron (2019) Electricity price forecasting, in “Wiley StatsRef: Statistics Reference Online”, Wiley, DOI: 10.1002/9781118445112.stat08215. Working paper version available from RePEc: https://ideas.repec.org/p/wuu/wpaper/hsc1901.html

Developed software components