Electric vehicles charging stations load forecasting based on hybrid XGBoost-BiLSTM model

Ahmad, N., Ghadi, Y., Adnan, M. & Ali, M. Load forecasting techniques for power system: research challenges and survey. IEEE Access 10, 71054–71090. https://doi.org/10.1109/ACCESS.2022.3187839 (2022).

Google Scholar 

Khan, S. Short-term electricity load forecasting using a new intelligence-based application. Sustainability https://doi.org/10.3390/su151612311 (2023).

Google Scholar 

Udendhran, R. et al. Transitioning to sustainable E-vehicle systems – Global perspectives on the challenges, policies, and opportunities. J. Hazard. Mater. Adv. 17, 100619. https://doi.org/10.1016/J.HAZADV.2025.100619 (2025).

Google Scholar 

Elahe, M. F., Kabir, M. A., Mahmud, S. M. H. & Azim, R. Factors impacting short-term load forecasting of charging station to electric vehicle. Electronics (Switzerland) https://doi.org/10.3390/electronics12010055 (2023).

Google Scholar 

Ran, J., Gong, Y., Hu, Y. & Cai, J. L. EV load forecasting using a refined CNN-LSTM-AM. Electr. Power Syst. Res. 238(August), 2025. https://doi.org/10.1016/j.epsr.2024.111091 (2024).

Google Scholar 

Van Kriekinge, G., De Cauwer, C., Sapountzoglou, N., Coosemans, T. & Messagie, M. Day-ahead forecast of electric vehicle charging demand with deep neural networks. World Electric Veh. J. https://doi.org/10.3390/wevj12040178 (2021).

Google Scholar 

Cheng, S., Wei, Z., Shang, D., Zhao, Z. & Chen, H. Charging load prediction and distribution network reliability evaluation considering electric vehicles’ spatial-temporal transfer randomness. IEEE Access 8, 124084–124096. https://doi.org/10.1109/ACCESS.2020.3006093 (2020).

Google Scholar 

S. Su, H. Zhao, H. Zhang, X. Lin, F. Yang, and Z. Li, Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning. In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017. https://doi.org/10.1109/ISAP.2017.8071382. (2017).

Tang, D. & Wang, P. Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles. IEEE Trans. Smart Grid 7(2), 627–636. https://doi.org/10.1109/TSG.2015.2437415 (2016).

Google Scholar 

Zhang, Q., Chen, J., Xiao, G., He, S. & Deng, K. TransformGraph: A novel short-term electricity net load forecasting model. Energy Rep. 9, 2705–2717. https://doi.org/10.1016/j.egyr.2023.01.050 (2023).

Google Scholar 

Gao, S. X., Liu, H. & Ota, J. Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms. Adv. Manuf 12(2), 227–251. https://doi.org/10.1007/S40436-023-00461-1/FIGURES/9 (2024).

Google Scholar 

Amini, M. H., Kargarian, A. & Karabasoglu, O. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electr. Power Syst. Res. 140, 378–390. https://doi.org/10.1016/J.EPSR.2016.06.003 (2016).

Google Scholar 

Louie, H. M. Time-series modeling of aggregated electric vehicle charging station load. Electr. Power Compon. Syst. 45(14), 1498–1511. https://doi.org/10.1080/15325008.2017.1336583 (2017).

Google Scholar 

A. Gautam, A. K. Verma, and M. Srivastava. A novel algorithm for scheduling of electric vehicle using adaptive load forecasting with vehicle-to-grid integration. In: 2019 8th International Conference on Power Systems: Transition towards Sustainable, Smart and Flexible Grids, ICPS 2019. https://doi.org/10.1109/ICPS48983.2019.9067702. (2019).

Almaghrebi, A., Aljuheshi, F., Rafaie, M., James, K. & Alahmad, M. Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods. Energies (Basel) https://doi.org/10.3390/en13164231 (2020).

Google Scholar 

Peng, Y. & Unluer, C. Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms. Resour. Conserv. Recycl. 190, 106812. https://doi.org/10.1016/j.resconrec.2022.106812 (2022).

Google Scholar 

Zhu, J., Yang, Z., Guo, Y., Zhang, J. & Yang, H. Short-term load forecasting for electric vehicle charging stations based on deep learning approaches. Appl. Sci. (Switzerland) https://doi.org/10.3390/app9091723 (2019).

Google Scholar 

Aduama, P., Zhang, Z. & Al-Sumaiti, A. S. Multi-feature data fusion-based load forecasting of electric vehicle charging stations using a deep learning model. Energies (Basel) https://doi.org/10.3390/en16031309 (2023).

Google Scholar 

Van Kriekinge, G., De Cauwer, C., Sapountzoglou, N., Coosemans, T. & Messagie, M. Day-ahead forecast of electric vehicle charging demand with deep neural networks. World Electr. Veh. J. 12(4), 178. https://doi.org/10.3390/WEVJ12040178 (2021).

Google Scholar 

Li, Y. et al. Probabilistic charging power forecast of EVCS: reinforcement learning assisted deep learning approach. IEEE Trans. Intell. Veh. 8(1), 344–357. https://doi.org/10.1109/TIV.2022.3168577 (2023).

Google Scholar 

Zhou, D. et al. Using Bayesian deep learning for electric vehicle charging station load forecasting. Energies 15(17), 6195. https://doi.org/10.3390/EN15176195 (2022).

Google Scholar 

Mohammad, F., Kang, D. K., Ahmed, M. A. & Kim, Y. C. Energy demand load forecasting for electric vehicle charging stations network based on ConvLSTM and BiConvLSTM architectures. IEEE Access 11, 67350–67369. https://doi.org/10.1109/ACCESS.2023.3274657 (2023).

Google Scholar 

Naveed, M. S. et al. Enhanced accuracy in solar irradiance forecasting through machine learning stack-based ensemble approach. Int. J. Green Energy https://doi.org/10.1080/15435075.2025.2450468 (2025).

Google Scholar 

Naveed, M. S. et al. Leveraging advanced AI algorithms with transformer-infused recurrent neural networks to optimize solar irradiance forecasting. Front. Energy Res. https://doi.org/10.3389/fenrg.2024.1485690 (2024).

Google Scholar 

Hanif, J. M. M. F. et al. The Solar AI Nexus: Reinforcement Learning Shaping the Future of Energy Management (Wiley, 2025).

Google Scholar 

D.-E. L. Yuvaraj Natarajan, Sri Preethaa K. R, Gitanjali Wadhwa, Young Choi, Zengshun Chen, D.-E. Lee, and Y. M. Scholar, SciProfilesScilitPreprints.orgGoogle. Enhancing building energy efficiency with IoT-driven hybrid deep learning models for accurate energy consumption prediction. Coimbatore 641407, India. https://www.mdpi.com/2071-1050/16/5/1925.

ACN-Dataset. https://ev.caltech.edu/dataset.

T. Chen, C. G.-P. of the 22nd acm sigkdd international, and undefined 2016. Xgboost: A scalable tree boosting system. dl.acm.orgT Chen, C GuestrinProceedings of the 22nd acm sigkdd international conference on knowledge, 2016•dl.acm.org, 13–17, 785–794. https://doi.org/10.1145/2939672.2939785. (2016).

Z. Huang, W. Xu, and K. Yu. Bidirectional LSTM-CRF Models for Sequence Tagging. http://arxiv.org/abs/1508.01991. (2015).