Energy Demand Prediction with Federated Learning for Electric Vehicle Networks

Abstract

In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering-based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63% and decrease communication overhead by 83.4% compared with other baseline machine learning algorithms.

AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform

“…Authors in [29] integrated RL method for EV bidding strategy. A novel centralized energy demand learning algorithm for EV energy demand prediction was introduced in [39]. Considering that most of these works carry out the experiments on a single centralized server, the system faces the following problems unavoidably.…”

“…Works in [31]- [34] employed deep learning techniques for energy generation and consumption forecast. In [35]- [39], intelligent integrated approaches were proposed for efficient demand response. However, the conventional aggregator in these approaches is equipped with a multi-GPU cluster, which requires high-power consumption and longterm maintenance [31], [35]- [37].…”

“…However, the conventional aggregator in these approaches is equipped with a multi-GPU cluster, which requires high-power consumption and longterm maintenance [31], [35]- [37]. Also, there is limited computing capability on local devices [38], [39], which remains a bottleneck for high-speed training. Although it is possible to have a custom-built system for local computing, it comes at a high expense and is not portable.…”

Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

“…however the authors have not considered the high mobility aspects of on-road vehicles and only propose an SY FL model. FL can also support utility applications using VNs such as the prediction of electric vehicles' energy demand, thus reducing energy transfer congestion at charging stations [19], resulting in improved on-road traffic efficiency. The charging data of vehicles at each station is essential for future energy demand prediction to pre-order and reserve electricity from the power grid suppliers.…”

“…Performance Optimization [17] A contract theoretic approach to cope with data asymmetry issues with the FL-based image recognition tasks in VNs. V2X-based Application [19] An FL-based energy demand algorithm developed to tackle energy transfer congestion issues of electric vehicles at charging stations.…”