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 …
Short-term load forecasting for electric vehicle charging stations based on deep learning approaches
Short-term load forecasting is a key task to maintain the stable and effective operation of
power systems, providing reasonable future load curve feeding to the unit commitment and …
power systems, providing reasonable future load curve feeding to the unit commitment and …
Ensemble learning for charging load forecasting of electric vehicle charging stations
Electric vehicles (EVs) can help reduce the dependency on fossil oil and increasing
concerns on environmental pollution problems. However, due to the complex charging …
concerns on environmental pollution problems. However, due to the complex charging …
Using bayesian deep learning for electric vehicle charging station load forecasting
D Zhou, Z Guo, Y Xie, Y Hu, D Jiang, Y Feng, D Liu - Energies, 2022 - mdpi.com
In recent years, replacing internal combustion engine vehicles with electric vehicles has
been a significant option for supporting reducing carbon emissions because of fossil fuel …
been a significant option for supporting reducing carbon emissions because of fossil fuel …
Electric vehicle charging load forecasting: A comparative study of deep learning approaches
Load forecasting is one of the major challenges of power system operation and is crucial to
the effective scheduling for economic dispatch at multiple time scales. Numerous load …
the effective scheduling for economic dispatch at multiple time scales. Numerous load …
Short-term electric vehicles charging load forecasting based on deep learning in low-quality data environments
X Shen, H Zhao, Y Xiang, P Lan, J Liu - Electric Power Systems Research, 2022 - Elsevier
The accurate prediction of electric vehicles (EVs) load is the research basis for evaluating
the impact of EVs on the power grid and optimizing the operation of the power grid …
the impact of EVs on the power grid and optimizing the operation of the power grid …
Aggregated electric vehicle fast-charging power demand analysis and forecast based on LSTM neural network
M Chang, S Bae, G Cha, J Yoo - Sustainability, 2021 - mdpi.com
With the widespread use of electric vehicles, their charging power demand has increased
and become a significant burden on power grids. The uncoordinated deployment of electric …
and become a significant burden on power grids. The uncoordinated deployment of electric …
Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques
G Vishnu, D Kaliyaperumal, PB Pati, A Karthick… - World Electric Vehicle …, 2023 - mdpi.com
Electric vehicles (EVs) are inducing revolutionary developments to the transportation and
power sectors. Their innumerable benefits are forcing nations to adopt this sustainable …
power sectors. Their innumerable benefits are forcing nations to adopt this sustainable …
Short-term electric vehicle charging demand prediction: A deep learning approach
S Wang, C Zhuge, C Shao, P Wang, X Yang, S Wang - Applied Energy, 2023 - Elsevier
Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to
the operation of EV fleets and charging stations. This paper develops a Long Short-Term …
the operation of EV fleets and charging stations. This paper develops a Long Short-Term …
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 …
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