Citywide Electric Vehicle Charging Demand Prediction Approach Considering Urban Region and Dynamic Influences
This work addresses the need for better charging infrastructure planning and vacant pile recommendations to support vehicle electrification, representing an incremental advancement in spatio-temporal prediction methods.
The paper tackles the problem of predicting citywide electric vehicle charging demand by addressing limitations in previous spatio-temporal studies, such as inadequate consideration of urban region attributes and dynamic influences, and demonstrates improved performance through experiments on a real dataset.
Electric vehicle charging demand prediction is important for vacant charging pile recommendation and charging infrastructure planning, thus facilitating vehicle electrification and green energy development. The performance of previous spatio-temporal studies is still far from satisfactory nowadays because urban region attributes and multivariate temporal influences are not adequately taken into account. To tackle these issues, we propose a learning approach for citywide electric vehicle charging demand prediction, named CityEVCP. To learn non-pairwise relationships in urban areas, we cluster service areas by the types and numbers of points of interest in the areas and develop attentive hypergraph networks accordingly. Graph attention mechanisms are employed for information propagation between neighboring areas. Additionally, we propose a variable selection network to adaptively learn dynamic auxiliary information and improve the Transformer encoder utilizing gated mechanisms for fluctuating charging time-series data. Experiments on a citywide electric vehicle charging dataset demonstrate the performances of our proposed approach compared with a broad range of competing baselines. Furthermore, we demonstrate the impact of dynamic influences on prediction results in different areas of the city and the effectiveness of our area clustering method.