Deep Learning for Energy Demand Forecasting in Electric Vehicle Charging Stations-Reference-Cited by-同舟云学术

Deep Learning for Energy Demand Forecasting in Electric Vehicle Charging Stations

Published:2024-01-18 Issue: Volume:3 Page:886-890
ISSN:
Container-title:2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)
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Author:

Hanumanthakari Sudheer¹,S Anita²,Christiana M. Beulah Viji³,Srinivasan Vellayan⁴,Kannan Sathish⁵,Kumar S.K. Nandha⁶

Affiliation:

1. ICFAI Foundation for Higher Education,Faculty of Science and Technology,Department of Electronics and Communication Engineering,Hyderabad,501203

2. R.M.K. Engineering College,Department of Electrical and Electronics Engineering,Kavaraipettai,Tamil Nadu,India,601206

3. Panimalar Engineering College,Department of Master of Business Administration,Chennai,Tamil Nadu,India,600123

4. S. A. Engineering College,Department of Master of Business Administration,Chennai,Tamil Nadu,India,600077

5. Saveetha Institute of Medical and Technical Sciences,Saveetha School of Engineering,Department of VLSI Microelectronics,Chennai,Tamil Nadu,India,602105

6. PSNA College of Engineering & Technology,Department of Electrical and Electronics Engineering,Dindigul,Tamil Nadu,India,624 622

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10493857/10493205/10493948.pdf?arnumber=10493948

Reference14 articles.

1. Renewable energy integration with electric vehicle technology: A review of the existing smart charging approaches

2. Impacts of COVID-19 on the electric vehicle industry: Evidence from China

3. Peer to Peer Distributed Energy Trading in Smart Grids: A Survey

4. Electric vehicle charging demand forecasting using deep learning model

5. An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand