发表机构
Foshan University; Jilin University; National University of Singapore（佛山科学技术学院; 吉林大学; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对城市气候压力增加电动汽车充电设施故障风险问题，开发FGDSE框架，通过划分特征家族、添加专家及门控机制预测故障风险，经SHAP归因等扩展为因果决策支持，实验表明其性能优越，能助力气候适应性维护与低碳出行。
AI 中文摘要
可靠的电动汽车充电基础设施是可持续低碳城市的基石，但城市气候压力增加了设备故障风险。从被动维修转向预防性维护需要准确的故障风险预测，这因多种信号的异质时间尺度和多周预测而复杂。我们开发了FGDSE，它将异质信号分为四个特征家族，为每个家族分配领域专家，并添加两个深度时间专家，通过水平门控机制预测1至30天的每日故障风险。SHAP归因和X-learner将概率输出扩展为因果决策支持。在13个站点25个月的数据上，FGDSE超过了十二个基线，在30天时保持约85%的宏召回率，AUC衰减仅3.2个点，揭示了主导因素从故障历史向气候压力的转变，确定极端高温是唯一因果效应随时间放大的因素，为气候适应性维护提供定量阈值，增强城市交通弹性并维持低碳出行。
英文摘要
Reliable electric vehicle (EV) charging infrastructure is a cornerstone of sustainable, low-carbon cities, yet urban climate stress such as extreme heat, heavy precipitation, and humidity increasingly raises equipment fault risk and undermines the resilience of urban energy and mobility services. Shifting operation from reactive repair to preventive maintenance depends on accurate, forward-looking fault-risk prediction, a task complicated by the heterogeneous time scales of physical, behavioral, contextual, and historical signals and by forecasting over a multi-week horizon. We develop FGDSE, a feature-governed dynamic stacking ensemble that forms an interpretable decision-support system for climate-resilient charging-asset management. It partitions heterogeneous signals into four feature families, assigns each to a domain expert whose inductive bias matches the data, and adds two deep temporal experts for short-term pulses and long-term degradation; a horizon-wise gating mechanism then learns adaptive weights to forecast daily fault risk over 1 to 30 days. SHAP attribution and an X-learner extend the probabilistic output into causal decision support with post-level treatment effects. On 25 months of data from 13 stations, FGDSE surpasses twelve baselines beyond the ten-day horizon, sustains about 85% macro-recall at 30 days with an AUC decay of only 3.2 points, and reveals a shift of dominance from fault history toward climate stress. It identifies extreme heat as the sole exposure whose causal effect amplifies over time, flagging roughly 30% of posts as heat-sensitive and yielding quantitative thresholds for climate-adaptive maintenance that strengthens urban mobility resilience and sustains low-carbon travel.
