Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)

Danting Cao; Jonathan Lerch; Daniel Stetter; Martin Neuburger; Ralf Wörner

doi:10.1051/e3sconf/202019104003

All issues

Volume 191 (2020)

E3S Web Conf., 191 (2020) 04003

References

Open Access

Issue		E3S Web Conf. Volume 191, 2020 2020 The 3^rd International Conference on Renewable Energy and Environment Engineering (REEE 2020)


Article Number		04003
Number of page(s)		6
Section		Modern Electronic Technology and Application
DOI		https://doi.org/10.1051/e3sconf/202019104003
Published online		24 September 2020

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[1] K. Mahmud, M. J. Hossain and J. Ravishankar, Peak-Load Management in Commercial Systems With Electric Vehicles, IEEE Systems Journal, vol. 13, no. 2, pp. 1872-1882 (2019) [Google Scholar]

[2] Q. Dang, Electric Vehicle (EV) Charging Management and Relieve Impacts in Grids, 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Charlotte, NC (2018) [Google Scholar]

[3] D. Yu, M. P. Adhikari, A. Guiral, A. S. Fung, F. Mohammadi and K. Raahemifar, The Impact of Charging Battery Electric Vehicles on the Load Profile in the Presence of Renewable Energy, 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada (2019) [Google Scholar]

[4] AS. Al-Ogaili, TJT. Hashim, NA. Rahmat, et al, Review on Scheduling, Clustering, and Forecasting Strategies for Controlling Electric Vehicle Charging: Challenges and Recommendations, IEEE Access, vol. 7 (2018) [Google Scholar]

[5] J. Zhu, Z. Yang, M. Mourshed, Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches, Energies, MDPI, Open Access Journal, vol. 12(14) (2019) [Google Scholar]

[6] ISO 15118-2 (CD, 2017), Edition 2: Road vehicles – Vehicle to grid communication interface – Part 2: Network and application protocol requirement [Google Scholar]

[7] Q. Sun, J. Liu, X. Rong, et al, Charging load forecasting of electric vehicle charging station based on support vector regression, 2016 IEEE PES Asia- Pacific Power and Energy Engineering Conference (APPEEC, Xi’an, 2016) [Google Scholar]

[8] Okfalisa, I. Gazalba, Mustakim and N. G. I. Reza, Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification, 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta (2017) [Google Scholar]

[9] J. Unpingco, Python for Probability Statistics, and Machine Learning, 2, pp. 214-218 ( Springer , 2019) [Google Scholar]

[10] W. Deng, Y. Guo, J. Liu, A missing power data filling method based on improved random forest algorithm, Chinese Journal of Electrical Engineering, vol. 5 (4) (2019) [Google Scholar]

[11] VK. Jain, A. Phophalia, M-ary Random Forest, Pattern Recognition and Machine Intelligence, 8th International Conference, PReMI 2019, Tezpur, India, December 17-20, 2019, Proceedings, Part II, pp.161-169 (Springer, 2019) [Google Scholar]

[12] D. Paper, Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, 1, pp. 110, 120 (Apress, 2020) [Google Scholar]

[13] A.V. Joshi, Machine Learning and Artificial Intelligence, 1, pp. 53-61 (Springer, 2020) [Google Scholar]

[14] G. Ke, Q. Meng T. Finely, et al, LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Advances in Neural Information Processing Systems 30 (NIP 2017) [Google Scholar]

[15] F. Hutter, L. Kotthoff, J. Vanschoren, Automated Machine Learning: Methods, Systems, Challenges, 1, pp. 3-8 (Springer, 2019) [Google Scholar]

[16] A. Jindal, M. Singh, N. Kumar, Consumption- Aware Data Analytical Demand Response Scheme for Peak Load Reduction in Smart Grid. IEEE Trans. Ind. Electron. (2018) [Google Scholar]

[17] H. Kaneko, Beware of r2 even for test datasets: Using the latest measured y‐values (r2LM) in time series data analysis, Journal of Chemometrics 33.2 (2018) [Google Scholar]

[18] J. Zhu, Z. Yang, Y. Chang, A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction, 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia, Chengdu, 20. [Google Scholar]