J. Kim, H. Cho, H. Lee, M.-S. Song, J.-H. Park, and H. Jung, “A Study on the Virtual Operation of Energy Storage Devices using the Electric Railway System HILS Platform,” The Transactions of The Korean Institute of Electrical Engineers, vol. 72, no. 10, pp. 1291~1298, 2023.DOI: 10.5370

C.-M. Yun, G.-J. Cho, H. Kim, and H. Jung, “A Study on the Train Brake Position-Based Control Method for Regenerative Inverters,” Energies, 2022.

J. Fabre et al., “Characterization and Implementation of Resonant Isolated DC/DC Converters for Future MVdc Railway Electrification Systems,” IEEE Transactions on Transportation Electrification, vol. 7, no. 2, pp. 854~869, June 2021.

Z. Tang, H. Yin, C. Yang, J. Yu, and H. Guo, “Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression,” Sustainable Cities and Society, vol. 66, pp. 1~10, 2021.

Z. Tang, H. Yin, C. Yang, J. Yu, and H. Guo, “Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression,” Sustainable Cities and Society, vol. 66, pp. 1~10, 2021.

I. N. Silva, D. H. Spatti, and R. A. Flauzino, Artificial Neural Networks, Springer, pp. 1~10, 2017.

K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 1~10, July 2016.

V. Veeramsetty, K. Reddy, M. Santhosh, et al., “Short-term electric power load forecasting using random forest and gated recurrent unit,” Electrical Engineering, vol. 104, pp. 307~329, 2022.

Q. Ma, H. Wang, P. Luo, Y. Peng, and Q. Li, “Ultra-short-term Railway traction load prediction based on DWT-TCN-PSO_SVR combined model,” International Journal of Electrical Power & Energy Systems, vol. 135, pp. 1~10, 2022.

V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines,” Ore Geology Reviews, vol. 71, pp. 1~10, 2015.

G.-F. Fan, M. Yu, S.-Q. Dong, Y.-H. Yeh, and W.-C. Hong, “Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling,” Utilities Policy, vol. 73, pp. 1~10, 2021.

N. J. J., K. Mohan, and G. Morten, “Relative evaluation of regression tools for urban area electrical energy demand forecasting,” Journal of Cleaner Production, vol. 218, pp. 1~10, 2019.