FMGCN: Federated Meta Learning-Augmented Graph Convolutional Network for EV Charging Demand Forecasting

Recent booming successes of electric vehicles (EVs) motivate emerging exploration of spatio-temporal (ST) EV charging demand forecasting to inform policy making. Recent studies have contributed to remarkable accuracy improvement by developing deep learning methods. However, when they access massive...

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Veröffentlicht in:	IEEE internet of things journal 2024-07, Vol.11 (14), p.24452-24466
Hauptverfasser:	You, Linlin, Chen, Qiyang, Qu, Haohao, Zhu, Rui, Yan, Jinyue, Santi, Paolo, Ratti, Carlo
Format:	Artikel
Sprache:	eng
Schlagworte:	Acceleration Artificial neural networks Charging demand forecasting Data exchange Data models Deep learning Demand forecasting Electric power demand Electric vehicle charging Electric vehicles Federated learning federated learning (FL) Forecasting graph convolution networks (GCNs) Internet of Things meta-learning Modules Predictive models Regional development Spatiotemporal data Training Urban areas
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