STR-GRU: A Spatio-Temporal Representation-Enhanced GRU for Short-Term Station-Level EV Charging Demand Forecasting

Accurate short-term station-level electric vehicle (EV) charging demand forecasting is important for efficient charging-station operation and local grid management. However, several challenges remain. First, charging demand exhibits overall demand evolution and rapid short-term variations that are difficult to model simultaneously. Second, demand varies across spatially distinct stations and exhibits recurring intraday and weekly patterns, requiring effective spatio-temporal representations to capture such characteristics. Existing methods commonly combine graph-based inter-station modeling with temporal modeling, but uneven station distributions complicate inter-station modeling and general temporal modeling may not fully capture recurring patterns. Third, electricity price and temperature provide complementary information, but their predictive relevance can differ across stations. To address these challenges, we propose a Spatio-Temporal Representation-Enhanced GRU (STR-GRU). The proposed model includes three core modules: LD-GRU, STRE, and AEFF. The Level-Difference Dual-Branch GRU (LD-GRU) separately encodes demand levels and first-order differences to model overall demand evolution and short-term variations. Spatio-Temporal Representation Enhancement (STRE) uses learnable station, time-of-day, and day-of-week embeddings to represent demand differences across spatially distinct stations and recurring temporal patterns without inter-station graph construction or information propagation. Adaptive Exogenous Feature Fusion (AEFF) adaptively fuses electricity price and temperature according to station-specific demand representations to account for differences in their predictive relevance. Experiments on six urban charging networks and three forecasting horizons demonstrate competitive forecasting performance and a favorable accuracy–efficiency trade-off.