EV Load Forecasting Methods That Consider the Dispatchable Potential in V2G System

Author:

Zhou Yuqing1,Yang Gaofeng1,Lei Yu1,Zhang Mingmei1,Yang Yulu1,Chen Hang1,Ye Hanxin1,Yu Qingguang2

Affiliation:

1. State Grid Chongqing Electric Power Company,Chonqing,China

2. Tsinghua University,Dept. of E.E,Beijing,China

Publisher

IEEE

Reference20 articles.

1. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

2. Network Fault Prediction Based on CNN-LSTM Hybrid Neural Network

3. A Double Channel CNNLSTM Model for Text Classification;liang;2020 IEEE 22nd International Conference on High Performance Computing and Communications IEEE 18th International Conference on Smart City IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS),2020

4. Enhanced Anisotropic Diffusion-based CNN-LSTM Architecture for Video Face Liveness Detection

5. CSVC-Net: Code-Switched Voice Command Classification using Deep CNN-LSTM Network

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1. Short- Term Electric Vehicle Demand Forecasts and Vehicle-to-Grid (V2G) Idle- Time Estimation Using Machine Learning;2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC);2023-03-08

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