Open AccessCharging Station Recommendation for Electric Vehicle Based on Federated Learning
Author(s) -
Xiaohui Wang,
Xiaokun Zheng,
Xiao Liang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1792/1/012055
Subject(s) - computer science , point (geometry) , federated learning , encryption , joint (building) , electric vehicle , factorization , data mining , artificial intelligence , computer security , algorithm , engineering , architectural engineering , power (physics) , physics , geometry , mathematics , quantum mechanics
At present, the usage of EV charging facilities is unbalanced. The accuracy of the charging station recommendation does not meet the demand. Due to the limitation of user privacy protection, charge point operators and vehicle enterprises cannot provide data to each other for joint analysis. Therefore, we proposed recommendation method of EV charge point based on federated learning. The federated factorization machine is implemented to make use of data features in both sides and cross features between them. We build the model by encrypted entity alignment, secure federated training and predicting. The experimental results show that the federated model improves the AUC of the model by 6% over those built with features only from the charge point operators. The model is superior to centralized LR-based and RF-based models. While the data does not need to leave the original platform, the model realizes the secure and precise federated charging point recommendation based on more comprehensive features.