STAR: Spatio-Temporal Agentic Reasoning for Interpretable Electric Vehicle Charging Demand Prediction

Abstract

Accurate prediction of electric vehicle charging demand is imperative for ensuring grid stability and optimizing urban mobility resources. While the emergence of large language models has introduced translation-based forecasting paradigms, existing methods typically suffer from numerical precision loss due to textual tokenization and fail to capture complex, non-Euclidean spatial dependencies. To address these limitations, this study introduces STAR, a spatio-temporal agentic reasoning framework that fundamentally redefines the forecasting task as a generative reasoning process. STAR integrates three core innovations, beginning with a temporal patching alignment mechanism that projects historical time-series segments into dense semantic vectors to preserve numerical fidelity. This is seamlessly combined with a graph-conditioned spatial context fusion module that empowers the agent to retrieve dynamic spatial dependencies via cross-attention-based topological fusion over an urban knowledge graph, thereby linking temporal dynamics with spatial causality. Finally, the framework employs an agentic chain-of-thought inference engine that mandates the generation of explicit reasoning traces by analyzing trends and synthesizing external factors prior to outputting the final forecast. Extensive experiments on ST-EVCDP, an open benchmark dataset collected from Shenzhen for urban EV charging demand prediction, demonstrate that STAR significantly outperforms state-of-the-art baselines, achieving a 27.3% to 41.9% prediction improvement for 60 min horizons compared to existing methods. Furthermore, the framework exhibits exceptional zero-shot cross-zone transferability across unseen traffic districts, providing interpretable decision support for critical infrastructure management.