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 amounts of data and frequently exchange data through the Internet of Things (IoT), data silos and inefficient training emerge as main challenges. To tackle these challenges, this study proposes an integrated approach for regional EV charging demand forecasting, named federated meta learning-based graph convolutional network, which consists of two modules, namely, 1) ST learning module, which introduces spatial and temporal attentions to capture the underlying charging patterns between different regions and cities effectively and 2) distributed pretraining module, which incorporates federated learning and meta-learning to enhance the adaptivity and generalisability of the forecasting model. A comprehensive evaluation based on a real-world data set of 25246 public EV charging piles shows that the proposed model outperforms other representative models with 1) an average improvement of 29.9% in forecasting errors; 2) an acceleration of 65% in convergence speed; and 3) a sound adaptability to support varying charging demand.