TopoBrick不用训练就能预测建筑传感器数据，靠知识图谱和智能采样选变量，比很多基础模型还准，跟专门训练的模型差不多。
TopoBrick是一个无需训练的零样本建筑IoT预测框架。它利用建筑知识图谱构建紧凑的结构骨架，并通过智能拓扑采样器为目标选择特定外生变量。在三个真实建筑上，TopoBrick超越了强零样本基础模型基线，并与全训练的建筑特定模型竞争。消融实验表明，拓扑感知采样比随机、仅本体或固定跳数选择更可靠，尤其对物理耦合的HVAC和天气驱动传感变量。
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
Building sensors are embedded in physical topology, spatial hierarchy, and operational context, yet existing forecasters often treat them as isolated time series or rely on fixed covariate sets. We present TopoBrick, a training-free framework for zero-shot building IoT (Internet-of-Things) forecasting. TopoBrick uses building knowledge graphs to construct a compact structural skeleton and employs an agentic topology sampler to select target-specific exogenous variables. The selected variables are organized by deployment-time availability, separating past-known sensor states from future-known calendar, schedule, and meteorological exogenous variables. Across three real-world buildings, TopoBrick outperforms strong zero-shot foundation-model baselines and remains competitive with fully trained building-specific models. Ablations show that topology-aware sampling is more reliable than random, ontology-only, or fixed-hop selection, especially for physically coupled HVAC and weather-driven sensing variables.