Education, Science, Technology, Innovation and Life
Open Access
Sign In

Analysis of Electric Vehicle Charging Behavior under Differential Privacy Protection

Download as PDF

DOI: 10.23977/isspj.2020.51003 | Downloads: 38 | Views: 2842

Author(s)

Baoyi Wang 1, Ziyi Liu 1, Shaomin Zhang 1

Affiliation(s)

1 School of Control and Computer Engineering, North China Electric Power University, HuaDian Road, Baoding, China

Corresponding Author

Baoyi Wang

ABSTRACT

In order to reduce the impact of electric vehicles on the power grid, the charging behavior of electric vehicles is analyzed, which can improve the reliability of power grid operation. Understanding the charging behavior of electric vehicle users is helpful to improve charging service and guide charging behavior. Aiming at the problem of privacy disclosure in the process of data analysis, this paper proposes an electric vehicle charging data clustering algorithm under differential privacy protection. This method enhances the availability of clustering result by improving the selection of initial cluster center points and detecting outliers. The classification of electric vehicle users is realized through experiment, and the characteristics of charging mode corresponding to each user are analyzed.

KEYWORDS

Electric Vehicles, Differential Privacy Protection, K-means Algorithm

CITE THIS PAPER

Baoyi Wang, Ziyi Liu and Shaomin Zhang. Analysis of Electric Vehicle Charging Behavior under Differential Privacy Protection. Information Systems and Signal Processing Journal (2020) 5: 11-17. DOI: http://dx.doi.org/10.23977/isspj.2020.51003.

REFERENCES

[1] Guo Jianlong, Wen Fushuan. The influence and countermeasures of electric vehicle charging on electric power system [J]. Power automation equipment, VOL: 35, NO. 6, Pages: 1-9+30, 2015.
[2] L. Lin, Y. Li and L. Ma, "Research on Clustering Analysis of New Energy Charging User Behavior Based on Spark," 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xi'an, China, 2018, pp. 23-28.
[3] X.Li et al., "A data-driven two-level clustering model for driving pattern analysis of electric vehicles and a case study", Journal of Cleaner Production, Vol.206, 2019, pp.827-837.
[4] C. Crozier, D. Apostolopoulou and M. McCulloch, "Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis," 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, 2018, pp. 1-6.
[5] Y. Wang et al., "The Analysis of Electrical Vehicles Charging Behavior Based on Charging Big Data," 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, China, 2019, pp. 63-67.
[6] J. Xiong et al., "Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1530-1540, April 2019.
[7] C. Clifton and T. Tassa, "On syntactic anonymity and differential privacy," 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), Brisbane, QLD, 2013, pp. 88-93.
[8] Wang Baoyi, Hu Heng, Zhang Shaomin. Power consumption data clustering analysis for mass users under differential privacy protection[J]. Power system automation, VOL: 42, NO. 2, Pages: 121-127, 2018.
[9] Zhang Lu, Li Guochang, Chen Yanxia, Sun Zhou, Wang Weixian, Tian Heping. Electric vehicle user segmentation and value evaluation method based on Data Mining [J]. Power system protection and control, VOL: 46, NO. 22, Pages: 124-130, 2018.

Downloads: 1006
Visits: 82742

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.