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Electricity price forecast on day-ahead market for mid- and short terms: capturing spikes in data sequences using recurrent neural network techniques

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Abstract

This paper aims to forecast the electricity prices in the day-ahead market (DAM) with complex recurrent neural networks (RNNs), which are powerful in predicting the sequential prices with lags of unknown duration between significant peaks in the price curve. Recently, the electricity markets have been shaken by random events, such as the COVID-19 pandemic or the conflict in Ukraine. Therefore, long short-term memory (LSTM), Gated Recurrent Unit (GRU) and echo state networks (ESNs) are more appropriate for memorizing random events that must be remembered after some time to adequately enhance the mid- and short-run forecast. Both methods overcome the vanishing gradient problem that is common for RNN using memory cells and gates that allow the updating of the memory and tracking long-term dependencies in the input sequence. Several time series prices from neighboring East European countries and the derivation of fundamental variables are combined to predict the electricity price in Romania. The input data cover 2019–2022. The best results were obtained for 2021, whereas the best solution is provided by bi-LSTM. The prediction is proven to be reliable for the next 3–4 days. The Mean Absolute Error (MAE) almost doubled in 2022, but to further improve the results, a higher number of neurons is taken for each layer and MAE decreased. Relative to ensemble models, there was a 12.81% reduction in MAE.

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Data availability

Data will be made available on request.

Notes

  1. https://www.opcom.ro/pp/grafice_ip/raportPIPsiVolumTranzactionat.php?lang=ro.

  2. https://hupx.hu/en/market-data/dam/historical-data.

  3. http://seepex-spot.rs/en/market-data/day-ahead-auction.

  4. https://ibex.bg/dam-history.php.

References

  1. Agathokleous C, Tuan LA and Steen D (2019) Stochastic operation scheduling model for a Swedish prosumer with PV and BESS in Nordic day-ahead electricity market. 2019 IEEE Milan PowerTech, PowerTech 2019. https://doi.org/10.1109/PTC.2019.8810651

  2. Bâra A, Oprea S-V (2024) Predicting day-ahead electricity market prices through the integration of macroeconomic factors and machine learning techniques. Int J Comput Intell Syst 17(1):10. https://doi.org/10.1007/s44196-023-00387-3

    Article  Google Scholar 

  3. Bashir N, Irwin D, Shenoy P (2021) A probabilistic approach to committing solar energy in day-ahead electricity markets. Sustain Comput: Inform Syst. https://doi.org/10.1016/j.suscom.2020.100477

    Article  Google Scholar 

  4. Beltrán S, Castro A, Irizar I, Naveran G, Yeregui I (2022) Framework for collaborative intelligence in forecasting day-ahead electricity price. Appl Energy. https://doi.org/10.1016/j.apenergy.2021.118049

    Article  Google Scholar 

  5. Busse J, Rieck J (2022) Mid-term energy cost-oriented flow shop scheduling: integration of electricity price forecasts, modeling, and solution procedures. Comput Ind Eng. https://doi.org/10.1016/j.cie.2021.107810

    Article  Google Scholar 

  6. Chai S, Xu Z, Jia Y (2019) Conditional density forecast of electricity price based on ensemble ELM and logistic EMOS. IEEE Trans Smart Grid. https://doi.org/10.1109/TSG.2018.2817284

    Article  Google Scholar 

  7. Dietrich K, Latorre JM, Olmos L, Ramos A (2015) Modelling and assessing the impacts of self supply and market-revenue driven Virtual Power Plants. Electric Power Syst Res. https://doi.org/10.1016/j.epsr.2014.10.015

    Article  Google Scholar 

  8. Edmond C (2022) How much energy does the EU import from Russia? World Economic Forum

  9. Fiuza de Bragança GG, Daglish T (2016) Can market power in the electricity spot market translate into market power in the hedge market? Energy Econ. https://doi.org/10.1016/j.eneco.2016.05.010

    Article  Google Scholar 

  10. Gabrielli P, Wüthrich M, Blume S, Sansavini G (2022) Data-driven modeling for long-term electricity price forecasting. Energy. https://doi.org/10.1016/j.energy.2022.123107

    Article  Google Scholar 

  11. Gianfreda A, Ravazzolo F, Rossini L (2020) Comparing the forecasting performances of linear models for electricity prices with high RES penetration. Int J Forecast. https://doi.org/10.1016/j.ijforecast.2019.11.002

    Article  Google Scholar 

  12. Gürtler M, Paulsen T (2018) The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany. Energy Econ. https://doi.org/10.1016/j.eneco.2018.07.006

    Article  Google Scholar 

  13. Haben S, Caudron J, Verma J (2021) Probabilistic day-ahead wholesale price forecast: a case study in Great Britain. Forecasting. https://doi.org/10.3390/forecast3030038

    Article  Google Scholar 

  14. Hubicka K, Marcjasz G, Weron R (2019) A note on averaging day-ahead electricity price forecasts across calibration windows. IEEE Trans Sustain Energy. https://doi.org/10.1109/TSTE.2018.2869557

    Article  Google Scholar 

  15. Adolfsen JF, Kuik F, Lis EM and Schuler T (2022) The impact of the war in Ukraine on euro area energy markets. Eur Central Bank Econ Bull

  16. Kath C, Ziel F (2018) The value of forecasts: quantifying the economic gains of accurate quarter-hourly electricity price forecasts. Energy Econ. https://doi.org/10.1016/j.eneco.2018.10.005

    Article  Google Scholar 

  17. Keles D, Scelle J, Paraschiv F, Fichtner W (2016) Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl Energy. https://doi.org/10.1016/j.apenergy.2015.09.087

    Article  Google Scholar 

  18. Lago J, de Ridder F, de Schutter B (2018) Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Appl Energy. https://doi.org/10.1016/j.apenergy.2018.02.069

    Article  Google Scholar 

  19. Lehna M, Scheller F, Herwartz H (2022) Forecasting day-ahead electricity prices: a comparison of time series and neural network models taking external regressors into account. Energy Econ. https://doi.org/10.1016/j.eneco.2021.105742

    Article  Google Scholar 

  20. Liu L, Bai F, Su C, Ma C, Yan R, Li H, Sun Q, Wennersten R (2022) Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model. Energy. https://doi.org/10.1016/j.energy.2022.123417

    Article  Google Scholar 

  21. Loutfi AA, Sun M, Loutfi I, Solibakke PB (2022) Empirical study of day-ahead electricity spot-price forecasting: Insights into a novel loss function for training neural networks. Appl Energy 319:119182. https://doi.org/10.1016/J.APENERGY.2022.119182

    Article  Google Scholar 

  22. Lu X, Qiu J, Lei G, Zhu J (2022) Scenarios modelling for forecasting day-ahead electricity prices: case studies in Australia. Appl Energy. https://doi.org/10.1016/j.apenergy.2021.118296

    Article  Google Scholar 

  23. Mazzi N, Kazempour J, Pinson P (2018) Price-taker offering strategy in electricity pay-as-bid markets. IEEE Trans Power Syst. https://doi.org/10.1109/TPWRS.2017.2737322

    Article  Google Scholar 

  24. Meng A, Wang P, Zhai G, Zeng C, Chen S, Yang X, Yin H (2022) Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization. Energy 254:124212. https://doi.org/10.1016/J.ENERGY.2022.124212

    Article  Google Scholar 

  25. Oprea SV, Bâra A, Preoţescu D, Bologa RA, Coroianu L (2020) A trading simulator model for the wholesale electricity market. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3029291

    Article  Google Scholar 

  26. Özen K, Yıldırım D (2021) Application of bagging in day-ahead electricity price forecasting and factor augmentation. Energy Econ. https://doi.org/10.1016/j.eneco.2021.105573

    Article  Google Scholar 

  27. Qussous R, Harder N, Weidlich A (2022) Understanding power market dynamics by reflecting market interrelations and flexibility-oriented bidding strategies. Energies. https://doi.org/10.3390/en15020494

    Article  Google Scholar 

  28. Romero Á, Dorronsoro JR, Díaz J (2019) Day-ahead price forecasting for the spanish electricity market. Int J Interact Multimed Artif Intell. https://doi.org/10.9781/ijimai.2018.04.008

    Article  Google Scholar 

  29. Sandhu HS, Fang L, Guan L (2016) Forecasting day-ahead price spikes for the Ontario electricity market. Electric Power Syst Res. https://doi.org/10.1016/j.epsr.2016.08.005

    Article  Google Scholar 

  30. Shafie-Khah M, Moghaddam MP, Sheikh-El-Eslami MK (2011) Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Convers Manage. https://doi.org/10.1016/j.enconman.2010.10.047

    Article  Google Scholar 

  31. Thompson H (2022) What does the war in Ukraine mean for the geopolitics of energy prices? Economics Observatory

  32. Tolefson J (2022) What the war in Ukraine means for energy, climate and food. Nature. https://doi.org/10.1038/d41586-022-00969-9

    Article  Google Scholar 

  33. Tschora L, Pierre E, Plantevit M, Robardet C (2022) Electricity price forecasting on the day-ahead market using machine learning. Appl Energy. https://doi.org/10.1016/j.apenergy.2022.118752

    Article  Google Scholar 

  34. Weron R (2014) Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int J Forecast. https://doi.org/10.1016/j.ijforecast.2014.08.008

    Article  Google Scholar 

  35. Yang H, Schell KR (2022) QCAE: a quadruple branch CNN autoencoder for real-time electricity price forecasting. Int J Electr Power Energy Syst 141:108092. https://doi.org/10.1016/J.IJEPES.2022.108092

    Article  Google Scholar 

  36. Zhang C, Li R, Shi H, Li F (2020) Deep learning for day-ahead electricity price forecasting. In IET Smart Grid. https://doi.org/10.1049/iet-stg.2019.0258

    Article  Google Scholar 

  37. Zhang JL, Zhang YJ, Li DZ, Tan ZF, Ji JF (2019) Forecasting day-ahead electricity prices using a new integrated model. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2018.08.025

    Article  Google Scholar 

  38. Zhang T, Tang Z, Wu J, Du X, Chen K (2022) Short term electricity price forecasting using a new hybrid model based on two-layer decomposition technique and ensemble learning. Electric Power Syst Res. https://doi.org/10.1016/j.epsr.2021.107762

    Article  Google Scholar 

  39. Ziel F, Weron R (2018) Day-ahead electricity price forecasting with high-dimensional structures: univariate vs. multivariate modeling frameworks. Energy Econ. https://doi.org/10.1016/j.eneco.2017.12.016

    Article  Google Scholar 

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Acknowledgements

This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI - UEFISCDI, project number COFUND-CETP-SMART- LEM-1, within PNCDI IV.

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Authors and Affiliations

  1. Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Bucharest, Romania

    Adela Bâra & Simona Vasilica Oprea

Authors
  1. Adela Bâra
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  2. Simona Vasilica Oprea
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Contributions

AB was involved in conceptualization, methodology, validation, formal analysis, investigation, writing—original draft, writing—review and editing and visualization. SVO provided software and contributed to conceptualization, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization, supervision and project administration.

Corresponding author

Correspondence to Simona Vasilica Oprea.

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The authors declare there is no conflict of interest.

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Appendices

Appendix A

See Tables

Table 5 Statistics for 2019
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Table 6 Statistics for 2020
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Table 7 Statistics for 2021
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Table 8 Statistics for 2022
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Appendix B

See Figs.

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EPF for February 2020 with GRU

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EPF for February 2021 with GRU

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EPF for February 2022 with GRU

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EPF for February 2020 with LSTM

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EPF for February 2021 with LSTM

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EPF for February 2022 with LSTM

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EPF for February 2020 with bi-LSTM

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EPF for February 2021 with bi-LSTM

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EPF for February 2022 with bi-LSTM

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Bâra, A., Oprea, S.V. Electricity price forecast on day-ahead market for mid- and short terms: capturing spikes in data sequences using recurrent neural network techniques. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02393-w

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