Keywords
day-ahead market
electricity price
neural networks
deep learning
microgrids
How to Cite
Abstract
The study presents a comparative analysis of short-term day-ahead electricity price forecasting models based on deep learning artificial neural networks. The research is conducted under the conditions of a unified trading zone of the Integrated Power System of Ukraine, synchronized with ENTSO-E, where the intraday price dynamics are shaped by high demand volatility, changing generation structure, regulatory constraints, and the consequences of attacks on the energy infrastructure. These factors lead to the emergence of nonlinear and locally structured patterns in the price time series, which complicates the application of traditional statistical forecasting methods. The study evaluates five deep learning architectures — LSTM, GRU, Transformer, MLP, and CNN — with the aim of assessing their effectiveness in modeling the relative price normalized by the hourly regulatory cap. The results demonstrate that forecasting accuracy is strongly dependent on the model’s ability to capture local structural patterns and adapt to stochastic transitions between market operating regimes. The convolutional neural network provides the lowest forecast error, indicating its suitability for operational planning and economic dispatching of microgrids, where the accuracy of price forecasts directly determines the effectiveness of decision-making. Ref. 19, fig. 2, tables 2.
References
1.Kyrylenko O., Denysiuk S., Blinov I. Digital transformation of the energy industry: current trends and task. Pratsi Instytutu Elektrodynamiky NAN Ukrainy. 2022. V. 65. Pp. 5-14. (Ukr) DOI: https://doi.org/10.15407/publishing2022.65.005
2.F. Jan, H. Iftikhar, M. Tahir, and M. Khan, Forecasting day-ahead electric power prices with functional data analysis, Frontiers in Energy Research, vol. 13, Mar. 2025. DOI: https://doi.org/10.3389/fenrg.2025.1477248.
3.F. Lisi and M. M. Pelagatti, Component estimation for electricity market data: Deterministic or stochastic? Energy Economics, Vol. 74, Pp. 13–37, Aug. 2018. DOI: https://doi.org/10.1016/j.eneco.2018.05.027
4.F. Jan, I. Shah, and S. Ali, Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis, Energies, Vol. 15, No. 9, P. 3423, May 2022. DOI: https://doi.org/10.3390/en15093423
5.H. Iftikhar, J. E. Turpo-Chaparro, Paulo Canas Rodrigues, and Javier Linkolk López-Gonzales, Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition–Combination Technique, Energies, Vol. 16, No. 18, Pp. 6669–6669, Sep. 2023. DOI: https://doi.org/10.3390/en16186669
6.I. Shah, H. Iftikhar, and S. Ali, Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches, Journal of Mathematics, Vol. 2022, Pp. 1–14, Jul. 2022. DOI: https://doi.org/10.1155/2022/3581037
7.I. Shah, H. Bibi, S. Ali, L. Wang, and Z. Yue, Forecasting One-Day-Ahead Electricity Prices for Italian Electricity Market Using Parametric and Nonparametric Approaches, IEEE Access, Vol. 8, Pp. 123104–123113, 2020. DOI: https://doi.org/10.1109/access.2020.3007189
8.R. Beigaite, T. Krilavičius, and Ka Lok Man, “Electricity Price Forecasting for Nord Pool Data,” International Conference on Platform Technology and Service, Jan. 2018, DOI: https://doi.org/10.1109/platcon.2018.8472762
9.J. P. S. Catalão, H. M. I. Pousinho, and V. M. F. Mendes, Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach, Energy Conversion and Management, Vol. 52, No. 2, Pp. 1061–1065, Feb. 2011. DOI: https://doi.org/10.1016/j.enconman.2010.08.035
10.Blinov I.V., Korkhmazov G.S. Using artificial neural networks to solve the problem of short-term forecasting of wholesale market prices for electric energy. Pratsi Instytutu Elektrodynamiky NAN Ukrainy. 2009. P. 15–22.
11.Blinov I.V., Korkhmazov G.S., Popovych V.I., Zozulya A.M. Short-term forecasting of wholesale prices for electric energy using radial-basis artificial neural networks. Pratsi Instytutu Elektrodynamiky NAN Ukrainy. 2009. Issue 24. Pp. 23–31.
12.Blinov I.V., Parus Ye.V., Shymaniuk P.V., Vorushylo A.O. Optimization model of microgrid functioning with solar power plant and energy storage system. Tekhnichna Elektrodynamika. 2024. (5): 69–78. DOI: https://doi.org/10.15407/techned2024.05.069
13.Kyrylenko O., Denysiuk S., Blinov I. Energy management: new priorities of the 21st century. Power engineering: economics, technique, ecology. 2024. (1): 7–27. https://doi.org/10.20535/1813-5420.1.2024.297508
14.Blinov I.V. Development of distributed energy in Ukraine using microgrid technologies (according to the materials of scientifi c report at the meeting of the Presidium of NAS of Ukraine, March 5, 2025). Visnyk Nac. Akad. Nauk Ukr. 2025. (5): 35–44. DOI: https://doi.org/10.15407/visn2025.05.035
15.Trading results on the DAM | Energy Map, Energy Map, 2019. https://energy-map.info/uk/datasets/5a616fba-fbc9-4073-9532-9161592faca8#sample (accessed Dec. 08, 2025).
16.S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, Vol. 9, No. 8, Pp. 1735–1780, Nov. 1997. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
17.K. Cho et al., Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. DOI: https://doi.org/10.3115/v1/d14-1179
18.A. Vaswani et al., Attention is all you need, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., Curran Associates, Inc., 2017. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
I. Koprinska, D. Wu, and Z. Wang, Convolutional Neural Networks for Energy Time Series Forecasting, IEEE Xplore, Jul. 01, 2018. DOI: https://doi.org/10.1109/IJCNN.2018.8489399
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