发表机构
University of New South Wales; Hong Kong University of Science and Technology (Guangzhou); MBZUAI; CSIRO Energy Centre（新南威尔士大学; 香港科技大学（广州）; 穆罕默德·本·扎耶德人工智能大学; 联邦科学与工业研究组织能源中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对建筑物联网预测中现有方法的不足，提出无训练框架TopoBrick，利用建筑知识图和智能拓扑采样器选择外源变量，按部署时间可用性组织，在三座真实建筑实验中性能出色，拓扑感知采样更可靠。
AI 中文摘要
建筑传感器嵌入在物理拓扑、空间层次结构和操作环境中，但现有预测器通常将它们视为孤立的时间序列或依赖于固定的协变量集。我们提出了TopoBrick，一个用于零样本建筑物联网预测的无训练框架。TopoBrick使用建筑知识图构建紧凑的结构骨架，并采用智能拓扑采样器选择特定目标的外源变量。所选变量按部署时间可用性组织，将过去已知的传感器状态与未来已知的日历、日程和气象外源变量分开。在三座真实建筑中，TopoBrick优于强大的零样本基础模型基线，并且与完全训练的特定建筑模型具有竞争力。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，特别是对于物理耦合的HVAC和天气驱动的传感变量。
英文摘要
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.
Comments13 pages, 4 figures, 4 tables
