Energy Demand Prediction with Federated Learning for Electric Vehicle Networks

By Yuris Mulya Saputra, Dinh Thai Hoang, Diep N. Nguyen, Eryk Dutkiewicz, M. Mueck, S. Srikanteswara. 2019

232 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

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Citations in the corpus

FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…to, computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], ﬁnance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and smart cities [110,…”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “The authors in [180] propose a federated energy demand learning (FEDL) approach to manage energy resources in charging stations (CSs) for electric vehicles (EVs).”
Local Differential Privacy-Based Federated Learning for Internet of Things (2020, methodology): “Recently, FL is explored extensively in the IoT recently [30]–[34].”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, methodology): “[45] use FL to conduct accurate energy demand prediction with low communication”
A Survey on federated learning* (2020, background): “Besides, FL is popular in IOT devices to overcome the challenge of functionality and connectivity such as energy demand prediction for electric vehicle network [51] and optimization for smart home architecture [52].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “eeds to develop new resource scheduling algorithm to moves the workload to the nodes with more computation power. Similar issues happen on other environments such as vehicle-tovehicle networks [180], [181]. 7.2 Conclusion Many efforts have been devoted to developing federated learning systems (FLSs)”
Decentralized Federated Learning: A Survey and Perspective (2023, unknown): “…its excellent capabilities in various areas, including intelligent transportation, Internet of Things (IoT), healthcare, manufacturing, agriculture, energy, remote sensing, and more [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks (2020, result): “Preliminary results in this paper have been presented at the IEEE GLOBECOM Conference, 2019 [1].”