ENERGY DEMAND FORECASTING FOR ELECTRIC VEHICLES USING  BLOCKCHAIN-BASED FEDERATED LEARNING

Dr. Gurudev Sawarkar ,DR. Shilpa R. Kalambe

ENERGY DEMAND FORECASTING FOR ELECTRIC VEHICLES USING BLOCKCHAIN-BASED FEDERATED LEARNING

Authors

Dr. Gurudev Sawarkar ,DR. Shilpa R. Kalambe

Keywords:

Electric Vehicles, Energy Demand Forecasting, Blockchain, Federated Learning, Smart Grid, Privacy Preservation, Distributed Learning, Artificial Intelligence

Abstract

The rapid adoption of electric vehicles (EVs) hassignificantly increased the demand for intelligentenergy management and accurate electricitydemand forecasting in modern smart grids.Conventional centralized forecasting approachesrequire large-scale data collection, raisingconcerns regarding data privacy, communicationoverhead, and security vulnerabilities. Blockchain based Federated Learning

References

[1] J. Hu, H. Morais, T. Sousa, and M. Lind, "Electric Vehicle Fleet Management in Smart Grids: A Review of Services, Optimization and Control Aspects," Renewable and Sustainable Energy Reviews, vol. 56, pp. 1207–1226, 2016

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Published

2024-12-20

How to Cite

Dr. Gurudev Sawarkar ,DR. Shilpa R. Kalambe. (2024). ENERGY DEMAND FORECASTING FOR ELECTRIC VEHICLES USING BLOCKCHAIN-BASED FEDERATED LEARNING. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 9385–9392. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5726