标题： InsightBuild：智能建筑系统中的基于大模型的因果推理 显示英文标题

标题： InsightBuild: LLM-Powered Causal Reasoning in Smart Building Systems

摘要： 智能建筑生成大量传感器和控制数据，但设施管理人员往往缺乏对异常能耗的清晰解释。 我们提出InsightBuild，一种两阶段框架，将因果分析与微调的大语言模型（LLM）相结合，以提供可读的人类因果解释。 首先，一个轻量级的因果推断模块对来自Google智能建筑和伯克利办公室数据集的建筑遥测数据（例如温度、暖通空调设置、占用情况）应用格兰杰因果检验和结构因果发现。 接下来，一个在对齐的传感器级原因和文本解释对上微调的LLM，接收检测到的因果关系作为输入，并生成简洁、可操作的解释。 我们在两个现实数据集上评估了InsightBuild（Google：2017-2022；Berkeley：2018-2020），使用专家标注的真实原因对保留的异常集合进行评估。 我们的结果表明，将显式因果发现与基于LLM的自然语言生成相结合可以产生清晰、精确的解释，帮助设施管理人员诊断和缓解能源低效问题。 显示英文摘要

摘要： Smart buildings generate vast streams of sensor and control data, but facility managers often lack clear explanations for anomalous energy usage. We propose InsightBuild, a two-stage framework that integrates causality analysis with a fine-tuned large language model (LLM) to provide human-readable, causal explanations of energy consumption patterns. First, a lightweight causal inference module applies Granger causality tests and structural causal discovery on building telemetry (e.g., temperature, HVAC settings, occupancy) drawn from Google Smart Buildings and Berkeley Office datasets. Next, an LLM, fine-tuned on aligned pairs of sensor-level causes and textual explanations, receives as input the detected causal relations and generates concise, actionable explanations. We evaluate InsightBuild on two real-world datasets (Google: 2017-2022; Berkeley: 2018-2020), using expert-annotated ground-truth causes for a held-out set of anomalies. Our results demonstrate that combining explicit causal discovery with LLM-based natural language generation yields clear, precise explanations that assist facility managers in diagnosing and mitigating energy inefficiencies.