Predictive Scheduling Framework for Electric Vehicles With Uncertainties of User Behaviors
Published in IEEE Internet of Things Journal, 2017
This paper presents a comprehensive predictive scheduling framework for electric vehicle charging that accounts for uncertainties in user behavior patterns. The framework integrates machine learning techniques to improve prediction accuracy and optimize charging schedules while considering grid constraints and user preferences.
Recommended citation: B. Wang, Y. Wang, H. Nazaripouya, C. Qiu, C. C. Chu, and R. Gadh, "Predictive Scheduling Framework for Electric Vehicles With Uncertainties of User Behaviors," IEEE Internet Things J., vol. 4, no. 1, pp. 52–63, Feb. 2017.
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