Energy Demand Load Forecasting for Electric Vehicle Charging Stations Network based on ConvLSTM and BiConvLSTM Architectures

The electrification of transport has proved to be a breakthrough to uplift the sustainable and eco-friendly platform in the global sector in which electric vehicles (EVs) are considered indispensable. In particular, creating intelligent energy management in the power distribution system integrated w...

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Veröffentlicht in:	IEEE access Jg. 11; S. 1
Hauptverfasser:	Mohammad, Faisal, Kang, Dong-Ki, Ahmed, Mohamed A., Kim, Young-Chon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	BiConvLSTM Charging stations Coders ConvLSTM Deep learning Demand forecasting Electric power demand Electric Vehicle Electric vehicle charging Electric vehicle charging stations Electric vehicles Electric Vehicles Charging Station Electrical loads Encoders-Decoders Energy Energy demand Forecasting Energy distribution Energy management EVCS dataset Forecasting Machine learning Mathematical models Predictive models Spatiotemporal data
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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