[HTML][HTML] Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks
Public charging station occupancy prediction plays key importance in developing a smart
charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However …
charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However …
Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model
X Zhang, KW Chan, H Li, H Wang… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting
has become a concern for planners and operators of EV charging stations (CSs). Due to the …
has become a concern for planners and operators of EV charging stations (CSs). Due to the …
Charging demand prediction in Beijing based on real-world electric vehicle data
J Zhang, Z Wang, EJ Miller, D Cui, P Liu… - Journal of Energy …, 2023 - Elsevier
The accurate estimation and prediction of charging demand play an essential role in
charging infrastructure planning, power grid laying and efficient operations. In this paper …
charging infrastructure planning, power grid laying and efficient operations. In this paper …
A systematic methodology for mid-and-long term electric vehicle charging load forecasting: The case study of Shenzhen, China
More and more adoptions of electric vehicles (EVs) would bring a potential threat on the
existing electric grid. In this context, a systematic methodology is presented in this paper to …
existing electric grid. In this context, a systematic methodology is presented in this paper to …
Self-attention-based machine theory of mind for electric vehicle charging demand forecast
The popularization of electric vehicles (EVs) and charging stations has been threatening the
distribution network's reliability and efficiency. The prediction of EV charging demand can …
distribution network's reliability and efficiency. The prediction of EV charging demand can …
Probabilistic charging power forecast of EVCS: Reinforcement learning assisted deep learning approach
The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely
deployed with the development of large-scale transportation electrifications. However, since …
deployed with the development of large-scale transportation electrifications. However, since …
A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network
T Zhang, Y Huang, H Liao, Y Liang - Applied Energy, 2023 - Elsevier
Due to the participation of large-scale electric vehicles (EVs) in Vehicle-to-Grid (V2G)
services, V2G dispatch centers need to predict the charging and discharging (C&D) loads of …
services, V2G dispatch centers need to predict the charging and discharging (C&D) loads of …
A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction
Short-term load forecasting is an important issue in energy management system and a key
measure to maintain the stable and effective operation of power systems, providing …
measure to maintain the stable and effective operation of power systems, providing …
Forecasting the EV charging load based on customer profile or station measurement?
In this paper, forecasting of the Electric Vehicle (EV) charging load has been based on two
different datasets: data from the customer profile (referred to as charging record) and data …
different datasets: data from the customer profile (referred to as charging record) and data …
Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q-Learning Technique
M Dabbaghjamanesh, A Moeini… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The electric vehicles'(EVs) rapid growth can potentially lead power grids to face new
challenges due to load profile changes. To this end, a new method is presented to forecast …
challenges due to load profile changes. To this end, a new method is presented to forecast …