期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 19, 期 3, 页码 3029-3038出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2022.3182972
关键词
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
This article proposes a blockchain-based personalized federated deep learning scheme, P-3, for privacy-preserving energy demands prediction in EV charging networks. The proposed scheme demonstrates superior accuracy in predicting real-time energy demands compared to state-of-the-art schemes, while achieving reasonably low computational costs.
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.
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