Block-FeDL: Electric Vehicle Charging Load Forecasting Using Federated Learning and Blockchain

The increased charging demand resulting from the rapid development of electric vehicles (EVs) poses various challenges to the stable operation of distribution networks and the smart grid. Due to stochastic EV charging behaviour, high charging demand at charging stations (CSs) elevates the load curve which may lead to a spatially imbalanced load demand. As such, forecasting the highly stochastic EV charging load considering an individual EV's unique charging behaviour can result in maintaining safe operation of the grid and distribution network. Therefore, in this work, we propose Block-FeDL, a blockchain-based Federated Learning (FL) approach for EV charging load forecasting considering private and sensitive charging information of each EV user. Thereafter, we use a Bidirectional Long Short Term Memory (BiLSTM) model within FeDL for predicting EV charging load patterns at each client. Moreover, instead of using a centralized server for global model aggregation, we use blockchain technology, where model aggregation is performed in a decentralized manner and local model parameters shared by FL clients can be validated and securely recorded. Lastly, the results show that Block-FeDL outperforms the second-best baseline method by 95%, 96%, and 77% in terms of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for forecasting EV charging load.