MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations

The penetration of electric vehicles (EV) has been increasing rapidly in recent years. Electric vehicle charging load poses a huge demand on the power grids. The forecasting for electric vehicle charging load, especially for the charging load of EV charging stations, is of significant importance for the safe operation of power grids. However, most of the existing forecasting methods fail to capture the long-term dependencies efficiently and assume the availability of a large amount of training data. Hence, they cannot address newly built charging stations with scarce historical charging load data. Meanwhile, most of the methods focus on point forecasting, which lacks risk consideration. In this work, we aim to leverage the benefits of Transformer-based models for EV charging forecasting. Specifically, we propos Probformer, a Transformer-based forecasting model for charging load forecasting. To enable Probformer to adapt fast to unseen environments, we further extend it to MetaProbformer, a meta-learning-based forecasting framework. Extensive experiments have been done on real-world datasets for both point forecasting and probabilistic forecasting. Experimental results show that our methods can consistently outperform baseline methods by a large margin.