用拓扑智能选变量，零样本预测建筑物联网数据更准
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
- 基于建筑知识图谱构建结构骨架，智能挑选相关外部变量
- 在3个真实建筑上超越主流零样本模型，接近定制模型表现
- 适合没数据但有建筑结构的智能楼宇预测场景
建筑传感器嵌入物理拓扑、空间层级与运行上下文，但现有预测模型常将其视为孤立时间序列或依赖固定协变量集。我们提出TopoBrick，一种无需训练的零样本建筑物联网预测框架。TopoBrick利用建筑知识图谱构建紧凑结构骨架，并通过代理式拓扑采样器选择目标特定的外生变量。所选变量按部署时可用性组织，区分已知历史传感器状态与未来已知的日历、日程及气象外生变量。在三个真实建筑上，TopoBrick优于强零样本基线模型，且与全训练的专用模型性能相当。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，尤其对物理耦合的暖通空调与气象驱动传感器变量效果显著。
原文摘要 · Abstract (English)
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.
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