Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
arXiv:2405.00742v2 Announce Type: replace-cross Abstract: Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion. However, existing methods either suffer from the data privacy issue and the susceptibility to cyberattacks or fail to consider the spatial correlation among different stations. To address these challenges, a federated graph learning approach involving multiple charging stations is proposed to collaborativ
The proliferation of EVs and smart grid technologies necessitates robust and secure forecasting methods for energy management, making solutions like federated learning increasingly vital.
Securing critical infrastructure like EV charging networks from cyberattacks while maintaining data privacy is crucial for grid stability and the acceleration of EV adoption.
The ability to more securely and privately forecast EV charging demand introduces a new paradigm for grid management and cybersecurity in distributed energy resources.
- · Smart Grid Operators
- · Electric Vehicle Manufacturers
- · Cybersecurity Firms
- · Urban Planners
- · Centralized Data Management Systems
- · Insecure Grid Infrastructure
- · Legacy Energy Forecasting Methods
More resilient and efficient EV charging infrastructure will be developed, improving user experience and grid stability.
Increased trust in distributed energy resources will accelerate investment and adoption of renewable energy and smart grid technologies.
The principles of secure federated learning could extend to other critical infrastructure sectors facing similar data privacy and cybersecurity challenges.
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