Optimization of Users EV Charging Data Using Convolutional Neural Network
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4071-4_53
Reference22 articles.
1. Ai S, Chakravorty A, Rong C (2018) Household EV charging demand prediction using machine and ensemble learning. In: Proceedings IEEE international conference energy internet (ICEI), May 2018, pp 163–68
2. Yang Y, Tan Z, Ren Y (2020) ‘Research on factors that influence the fast-charging behavior of private battery electric vehicles.’ Sustainability 12(8):3439. https://doi.org/10.3390/su12083439
3. Venticinque S, Nacchia S (2019) Learning and prediction of E-car charging requirements for flexible loads shifting. In: Internet and distributed computing systems. Cham, Switzerland, Springer, pp 284–293
4. Frendo O, Graf J, Gaertner N, Stuckenschmidt H (2020) Data-driven smart charging for heterogeneous electric vehicle fleets. Energy AI 1:100007. https://doi.org/10.1016/j.egyai.2020.100007
5. Mies J, Helmus J, van den Hoed R (2018) ‘Estimating the charging profile of individual charge sessions of electric vehicles in The Netherlands.’ World Electr. Vehicle J. 9(2):17. https://doi.org/10.3390/wevj9020017
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