P-3: Privacy-Preserving Prediction of Real-Time Energy Demands in EV Charging Networks

【Author】 Li, Beibei; Guo, Yuqing; Du, Qingyun; Zhu, Ziqing; Li, Xiaohui; Lu, Rongxing

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】and accurate prediction of charging pile energy demands in electric vehicle (EV) charging networks contributes significantly to load shedding and energy conservation. However, existing methods usually suffer from either data privacy leakage problems or heavy communication overheads. In this article, we propose a novel blockchain-based personalized federated deep learning scheme, coined P-3, for privacy-preserving energy demands prediction in EV charging networks. Specifically, we first design an accurate deep learning-based energy demands prediction model for charging piles, by making use of the CNN, BiLSTM, and attention mechanism. Second, we develop a blockchain-based hierarchical and personalized federated learning framework with a consensus committee, allowing charging piles to collectively establish a comprehensive energy demands prediction model in a low-latency and privacy-preserving way. Last, a CKKS cryptosystem based secure communication protocol is crafted to guarantee the confidentiality of model parameters while model training. Extensive experiments on two real-world datasets demonstrate the superiorities of the proposed P3 scheme in accurately predicting real-time energy demands over state-of-the-art schemes. Further, the P-3 scheme can achieve reasonably low computational costs, compared with other homomorphic-based schemes, such as Paillier and BFV.

【Keywords】Predictive models; Collaborative work; Hidden Markov models; Data models; Electric vehicles; Load modeling; Electric vehicle charging; Blockchain; CKKS homomorphic encryption; electric vehicle (EV) charging networks; energy demands prediction; federated learning; privacy preservation

【发表时间】2023 MAR

【收录时间】2023-05-03

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-能源领域