Energy Demand Prediction with Federated Learning for Electric Vehicle Networks (1909.00907v1)

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.

• The paper introduces centralized, federated, and clustering-based learning approaches for electric vehicle energy demand prediction, focusing on accuracy, privacy, and communication efficiency.
• The federated learning approach significantly reduces communication overhead by 83.4% compared to centralized data aggregation, enhancing data privacy for charging stations.
• Clustering-based learning improves prediction accuracy by up to 24.63% compared to baseline models, leveraging geographical characteristics to minimize error biases.

Energy Demand Prediction with Federated Learning for Electric Vehicle Networks

The paper "Energy Demand Prediction with Federated Learning for Electric Vehicle Networks" investigates innovative methodologies aimed at predicting energy demand in electric vehicle (EV) networks using advanced machine learning techniques, specifically focusing on enhancing prediction accuracy while curtailing communication overhead and safeguarding data privacy. The research introduces three primary machine learning approaches: centralized energy demand learning (EDL), federated energy demand learning (FEDL), and clustering-based EDL.

The centralized EDL involves a Charging Station Provider (CSP) aggregating data from various charging stations (CSs) to conduct deep learning processes for predicting energy demand in a given area. Although effective, this method inherently incurs high communication overhead and raises privacy concerns due to frequent data sharing requirements. To mitigate these issues, the paper proposes the FEDL approach, where CSs train models locally using their datasets and share the trained models rather than raw data with the CSP, thereby significantly reducing communication overhead by 83.4% and enhancing privacy protection. Further improvement in prediction accuracy is achieved through clustering-based EDL, which groups CSs into clusters based on location, optimizing feature classification and minimizing prediction biases.

Experimentation using real-world data from Dundee City, the United Kingdom (2017-2018), demonstrates the proposed methodologies' efficacy. The EDL approaches provide a substantial reduction in prediction error, improving accuracy by up to 24.63% compared to baseline machine learning algorithms like Random Forest (RF), Support Vector Regression (SVR), and others. The clustering-based EDL particularly showcases the advantage of reducing error costs associated with biased predictions, thanks to its ability to leverage feature classification based on geographical characteristics.

The implications of this research are far-reaching both theoretically and practically. On a theoretical front, the research underscores the potential of federated learning frameworks in scenarios where data privacy is paramount, advancing the discourse on decentralized machine learning processes. Practically, the integration of federated learning into EV networks can prompt CSPs to adopt more sophisticated strategies, optimizing energy demand predictions and operational efficiency while mitigating privacy risks and communication bottlenecks.

Future developments may focus on further refining the machine learning models used, perhaps through introducing additional state-of-the-art deep learning architectures or optimizing feature classification methods. Additionally, exploring more extensive datasets and diverse geographic locations could offer insightful perspectives on adaptative clustering strategies, enhancing model transferability across global EV infrastructures.

In summary, the paper contributes essential advancements in the intersection of machine learning and electric vehicle network optimization, establishing a foundational understanding for deploying federated learning techniques in real-world environments characterized by complexity and privacy constraints.