Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network
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Cited by:
- Zhouquan Wu & Pradeep Krishna Bhat & Bo Chen, 2023. "Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks," Energies, MDPI, vol. 16(5), pages 1-20, March.
- Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
- Sabbir Ahmed & Sameera Mubarak & Jia Tina Du & Santoso Wibowo, 2022. "Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
- Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
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Keywords
deep learning; electric vehicle; fast-charging power demand; forecasting model; power system; road transport; sustainable transportation;All these keywords.
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