ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment-Reference-Cited by-同舟云学术

ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment

Published:2024 Issue: Volume:12 Page:23319-23337
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ramoliya Fenil¹^ORCID,Trivedi Chinmay¹^ORCID,Darji Krisha¹,Kakkar Riya¹^ORCID,Gupta Rajesh¹^ORCID,Tanwar Sudeep¹^ORCID,Polkowski Zdzislaw²,Alqahtani Fayez³^ORCID,Tolba Amr⁴^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

2. Department of Humanities and Social Sciences, The Karkonosze University of Applied Sciences, Jelenia Góra, Poland

3. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

4. Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

Funder

King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10431772.pdf?arnumber=10431772

Reference39 articles.

1. A Cooperative Hierarchical Multi-Agent System for EV Charging Scheduling in Presence of Multiple Charging Stations

2. Hierarchical Coupled Driving-and-Charging Model of Electric Vehicles, Stations and Grid Operators

3. On-Line Building Energy Optimization Using Deep Reinforcement Learning

4. Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

5. Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey

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