EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 372 (2026) EPJ Web Conf., 372 (2026) 07008 References
Open Access
Issue
EPJ Web Conf.
Volume 372, 2026
Advanced Power Systems (APS 2026)
Article Number 07008
Number of page(s) 7
Section Power and Energy Systems
DOI https://doi.org/10.1051/epjconf/202637207008
Published online 11 June 2026
  1. J. Mei, et al., Stochastic optimization of multi- energy system operation considering hydrogen- based vehicle applications. Adv. Appl. Energy 2, 100031 (2021). [Google Scholar]
  2. L. Tschora, et al., Electricity price forecasting on the day-ahead market using machine learning. Appl. Energy 313, 118752 (2022). [Google Scholar]
  3. A. Jędrzejewski, J. Lago, G. Marcjasz, R. Weron, Electricity price forecasting: The dawn of machine learning. IEEE Power Energy Mag. 20(3), 24–31 (2022). https://doi.org/10.1109/MPE.2022.3150809 [Google Scholar]
  4. S. Albahli, M. Shiraz, N. Ayub, Electricity price forecasting for cloud computing using an enhanced machine learning model. IEEE Access 8, 200971–200981 (2020). https://doi.org/10.1109/ACCESS.2020.3035328 [Google Scholar]
  5. A. Yousefi, O. A. Sianaki, D. Sharafi, Long-term electricity price forecast using machine learning techniques, in Proceedings of the IEEE ISGT Asia Conference, Chengdu, China (2019), 2909–2913. https://doi.org/10.1109/ISGT-Asia.2019.8881604 [Google Scholar]
  6. U. Ugurlu, I. Oksuz, O. Tas, Electricity price forecasting using recurrent neural networks. Energies 11(5), 1255 (2018). [Google Scholar]
  7. C. Zhang, et al., Deep learning for day-ahead electricity price forecasting. IET Smart Grid 3(4), 462–469 (2020). [Google Scholar]
  8. A. N. Alkawaz, A. Abdellatif, J. Kanesan, A. S. M. Khairuddin, H. M. Gheni, Day-ahead electricity price forecasting based on hybrid regression model. IEEE Access 10, 108021–108033 (2022). https://doi.org/10.1109/ACCESS.2022.3213081 [Google Scholar]
  9. A. Kitsatoglou, et al., An ensemble approach for enhanced day-ahead price forecasting in electricity markets. Expert Syst. Appl. 256, 124971 (2024). [Google Scholar]
  10. A. V. Özcan, et al., Forecasting day-ahead electricity prices for the electricity market with dynamic time period. Energy 338, 138766 (2025). [Google Scholar]
  11. J. Nowotarski, R. Weron, Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renew. Sustain. Energy Rev. 81, 1548–1568 (2018). [Google Scholar]
  12. S. Voronin, J. Partanen, Forecasting electricity price and demand using a hybrid approach based on wavelet transform, ARIMA and neural networks. Int. J. Energy Res. 38(5), 626–637 (2014). [Google Scholar]
  13. J. Lago, G. Marcjasz, B. De Schutter, R. Weron, Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. arXiv preprint arXiv:2008.08004 (2020). [Google Scholar]
  14. M. Castelli, A. Groznik, A. Popović, Forecasting electricity prices: A machine learning approach. Algorithms 13(5), 119 (2020). [Google Scholar]
  15. B. K. Mishra, V. Preniqi, D. Thakker, E. Feigl, Machine learning and deep learning prediction models for time-series: A comparative analytical study for the use case of the UK short-term electricity price prediction. Discover Internet Things 4, 24 (2024). [Google Scholar]
  16. C. Naumzik, S. Feuerriegel, Forecasting electricity prices with machine learning: Predictor sensitivity. arXiv preprint arXiv:2005.08005 (2020). [Google Scholar]
  17. Z. Dobesova, Evaluation of Orange data mining software and examples for lecturing machine learning tasks in geoinformatics. Comput. Appl. Eng. Educ. 32(4), e22735 (2024). [Google Scholar]
  18. P. F. Smith, S. Ganesh, P. Liu, A comparison of random forest regression and multiple linear regression for prediction in neuroscience. J. Neurosci. Methods 220(1), 85–91 (2013). [CrossRef] [Google Scholar]
  19. R. J. Hyndman, A. B. Koehler, Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.