Heterogeneous Components Fusion Network for Load Forecasting of Charging Stations

Kai Li, Fei Yu, Cheng Feng, Tian Xia

Abstract: Accurate load forecasting of charging stations enable managers to reduce the drivers' waiting time and operating costs. But the existing works for spatial-temporal sequence forecasting usually assume the spatial-continuity of signals. However, the recharging scenario, in which the above assumptions are not valid due to the sparse spatial distribution of stations, need further research. To fill the gap, we present a Heterogeneous Components Fusion Network to model dual components sourced from the planned and the unplanned recharging events independently. For planned recharging component, we design a customized transformer to 'looks up' the reference 'memory' for the prediction. And we propose the time-variant graph to model highly dynamic unplanned events. Experiments conducted on a load reading dataset of 120 stations suggest that our model achieves better performance than a series of state-of-the-arts for spatial-temporal sequence prediction problem.