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

Demand Response Recommendation Scheme with Privacy Protection for Users in Smart Grid

Download as PDF

DOI: 10.23977/cpcs.2022.060107 | Downloads: 8 | Views: 702

Author(s)

Shaomin Zhang 1, Xuechun Wang 1, Baoyi Wang 1

Affiliation(s)

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

Corresponding Author

Shaomin Zhang

ABSTRACT

In order to adapt to the randomness of new energy power generation, the diversity of residential electricity consumption behavior and the reform of the power trading system, it is necessary to improve the real-time demand response and the information interaction between the power grid and users. This paper firstly applies cluster analysis in the field of demand response to distinguish user groups with different electricity consumption characteristics, and then evaluates the potential of different user groups to participate in demand response, and uses group recommender system strategy to make corresponding recommendations. In addition, considering that a large amount of user historical data needs to be collected in the group recommender system to realize the recommendation for user groups, there may be the risk of leaking user privacy. This paper proposes a privacy protection demand response group based on implicit feedback. The recommendation model realizes the privacy protection of users in the group during the recommendation process.

KEYWORDS

smart grid,demand response,privacy protection,group recommender system

CITE THIS PAPER

Shaomin Zhang, Xuechun Wang and Baoyi Wang, Demand Response Recommendation Scheme with Privacy Protection for Users in Smart Grid. Computing, Performance and Communication Systems (2022) Vol. 6: 45-50. DOI: http://dx.doi.org/10.23977/cpcs.2022.060107.

REFERENCES

[1]Bhattarai B P ,M Lévesque, Bak-Jensen B ,et al. Design and Co-simulation of Hierarchical Architecture for Demand Response Control and Coordination[J].IEEE Transactions on Industrial Informatics, 2017, 13(4):1806-1816.
[2]B.Sharma, N.Gupta, K.R.Niazi, A.Swarnkar and S.Vashisth. Demand Response in the Global Arena: Challenges and Future Trends[J].2019 8th International Conference on Power Systems (ICPS), 2019, pp.1-5.
[3]D Li, Sun H , Chiu W Y , et al.Multiobjective Optimization for Demand Side Management in Smart Grid[J].IEEE Transactions on Industrial Informatics, 2018:1-1.
[4]Dara S , Chowdary C R , Kumar C .A survey on group recommender systems[J].Journal of Intelligent Information Systems, 2019.
[5]Choudhary N,Bharadwaj K K .Preference-Oriented Group Recommender System: SIGMA 2018, Volume 1[M].2019.
[6]D.Yang, B.Qu, and P.Cudré-Mauroux. Privacy-preserving social media data publishing for personalized ranking-based recommendation[J].IEEE Transactions on Knowledge and Data Engineering, vol.31, no.3, pp.507-520, 2019.
[7]C.Xu, J.Wang, L.Zhu, C.Zhang, and K.Sharif. Ppmr: a privacy-preserving online medical service recommendation scheme in ehealthcare system[J]. IEEE Internet of Things Journal, vol.6, no.3, pp.5665-5673, 2019.
[8]He Y,Zhang K,Wang H,et al.Impact factor-based group recommender system scheme with privacy preservation in MSNs[C] IEEE International Conference on Communications ( ICC),2017:1-6.
[9]Wang Hai-yan,Lu Jin-xiang.Personalized privacy protection method for group recommendation[J].Journal on Communications, 2019,40-(9) :106-115.
[10]H.Shin, S.Kim, J.Shin, and X.Xiao. Privacy enhanced matrix factorization for recommendation with local differential privacy[J].IEEE Transactions on Knowledge and Data Engineering, vol.30, no.9, pp.1770-1782, 2018.

Downloads: 1915
Visits: 97889

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.