Multidemand Forecasting for Electric Vehicle Charging Stations Under Time-of-Use Strategy via Attention-Based Deep Neural Network

Abstract

Electric vehicle charging stations (EVCSs) have become a pivotal infrastructure within the electric vehicle (EV) industry. In particular, many EV companies construct self-owned EVCSs to provide better charging service for their customers. For these self-owned EVCSs, to ensure the quality of service for self-owned users and third-party users, dynamic pricing based on the time-of-use (TOU) strategy has been extensively employed. This makes the demand forecasting of EVCSs important since it depicts the relationship between the charging price and the demand of an EVCS. Unfortunately, the existing techniques cannot accurately predict the demand of multiple users simultaneously. Consequently, this article examines the problem of multidemand forecasting of EVCSs, and proposes an efficient method to resolve this issue. The key insight of the proposed method is to train a deep neural network consisting of two subnetworks that can jointly forecast the demand of the self-owned user and the third-party user simultaneously. First, six kinds of features of EVCSs are extracted. Then, a novel deep neural network Atlas based on the attention mechanism is proposed to forecast the multidemand of EVCSs under the TOU strategy. Finally, to resolve the scarcity of historical charging demand data, a coarse-fine training process is proposed to train Atlas for each EVCS. The evaluation based on the real-world dataset of 771 EVCSs from an EV company demonstrates that Atlas significantly outperforms seven state-of-the-art techniques by up to 34.82% $\sim ~61.92$ %.