Abstract :

The Rapid Adoption Of Electric Vehicles (EVs) Has Significantly Increased The Complexity Of Energy Demand Management In Modern Power Systems. Accurate Energy Demand Forecasting Is Essential For Ensuring Efficient Charging Infrastructure Utilization, Grid Stability, And Sustainable Energy Distribution. However, Traditional Forecasting Approaches Often Face Challenges Related To Data Privacy, Decentralized Data Ownership, And Limited Collaboration Among Stakeholders. This Paper Proposes An Energy Demand Forecasting Framework For Electric Vehicles Using Blockchainbased Federated Learning. The Proposed Model Enables Multiple Charging Stations, Utility Providers, And EV Networks To Collaboratively Train Forecasting Models Without Sharing Raw Data, Thereby Preserving Data Privacy And Security. Federated Learning Facilitates Decentralized Model Training, While Blockchain Technology Ensures Transparency, Trust, Data Integrity, And Secure Communication Among Participating Entities. Advanced Machine Learning Algorithms Are Utilized To Analyze Historical Charging Patterns, Vehicle Usage Behavior, Weather Conditions, And Energy Consumption Data To Generate Accurate Demand Forecasts. Experimental Analysis Demonstrates That The Integration Of Blockchain And Federated Learning Improves Forecasting Accuracy, Enhances Data Security, And Reduces Communication Vulnerabilities. The Proposed Framework Provides An Efficient, Scalable, And Privacy-preserving Solution For Intelligent Energy Management In Future Electric Vehicle Ecosystems.

Published:

26-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

380-390

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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