Abstract :

The Rapid Growth Of Electric Vehicles (EVs) Has Created Significant Challenges For Power Grid Management And Charging Infrastructure Planning. Accurate Forecasting Of Electric Vehicle Charging Demand Is Essential For Efficient Energy Distribution, Load Balancing, And The Development Of Sustainable Smart Grid Systems. This Study Proposes A Smart Forecasting Framework For Electric Vehicle Charging Demand Using Hierarchical Probabilistic Techniques To Provide Reliable And Scalable Predictions Across Multiple Levels Of Aggregation. The Proposed System Analyzes Historical EV Charging Data, Temporal Patterns, Geographic Information, And Environmental Factors To Predict Future Charging Demand. A Hierarchical Forecasting Approach Is Used To Model Demand At Different Levels, Such As Individual Charging Stations, Regional Networks, And Overall Grid Demand. Probabilistic Forecasting Techniques Are Applied To Capture Uncertainty In Charging Behavior And Provide Prediction Intervals Rather Than Single Deterministic Values. This Improves The Reliability Of Demand Estimation For Energy Management Systems. Advanced Machine Learning And Statistical Models Are Integrated To Process Large Datasets And Identify Complex Patterns In EV Charging Behavior. The Hierarchical Structure Ensures Consistency Between Forecasts At Different Aggregation Levels, While Probabilistic Modeling Enables Better Risk Assessment And Decisionmaking For Grid Operators And Infrastructure Planners. The Proposed Framework Enhances The Accuracy And Interpretability Of EV Charging Demand Predictions, Supporting Smart Grid Optimization, Efficient Energy Allocation, And Strategic Planning Of Charging Infrastructure. This Approach Contributes To The Development Of Intelligent Transportation Systems And Sustainable Energy Management In The Rapidly Evolving Electric Mobility Ecosystem.

Published:

27-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

754-759

Section:

Articles

License:

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

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