InsightBuild：智能建筑系统中的LLM驱动因果推理
InsightBuild: LLM-Powered Causal Reasoning in Smart Building Systems
AI总结：
本文提出InsightBuild框架，结合因果推断与微调LLM，为智能建筑异常能耗生成可读的因果解释，助力管理者诊断和缓解能效问题。
摘要：
智能建筑产生大量传感器与控制数据流，但设施管理者常缺乏对异常能耗的清晰解释。我们提出InsightBuild，一个两阶段框架，将因果分析与微调后的大语言模型（LLM）结合，以提供人类可读的能耗模式因果解释。首先，轻量级因果推断模块对来自Google Smart Buildings和Berkeley Office数据集的建筑遥测数据（如温度、HVAC设置、占用率）应用Granger因果检验和结构因果发现。接着，在传感器级原因与文本解释对齐数据对上微调的LLM，接收检测到的因果关系作为输入，生成简洁、可操作的说明。我们在两个真实数据集（Google：2017-2022；Berkeley：2018-2020）上评估InsightBuild，对留出异常集使用专家标注的真实原因。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，能产生清晰、精确的解释，协助设施管理者诊断并缓解能源低效问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
