ReCo：部署约束下何时重新安置传感器套件——一项非侵入式负荷监测案例研究
ReCo: When to Relocate Sensor Kits under Deployment Constraints -- A NILM Case Study
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中文总结 AI 辅助
针对传感器套件数量有限且移动有停机成本的部署问题，本文提出基于约束的重新安置框架ReCo，通过覆盖增益决策去留，在NILM数据集上优于现有调度方法。
中文摘要 AI 辅助
许多感知任务仅通过在现场部署仪器来获取训练标签。在传感器套件数量有限、收集截止日期以及每次移动都存在测量停机时间的条件下，收集者必须反复决定是留在当前站点还是重新安置。我们在非侵入式负荷监测（NILM）中研究这一决策，该技术从家庭主电表估算单个电器的功率消耗，并利用临时安装电器级子电表的家庭数据进行训练。在NILM中，电器使用情况因电器类型、季节和气候而异，新数据的价值取决于目标运行与背景负荷组合的多样性。为解决此问题，我们提出了一种基于约束的重新安置框架，并将其实例化为NILM的ReCo（通过覆盖增益进行重新安置）。ReCo在联合目标-背景特征空间中统计新的运行状态，根据迄今收集的数据预测每个家庭未来的增益，并在每晚权衡停机后留在原地的增益与移至他处的增益。在Plegma数据集上，针对两种套件数量和两种停机成本的重放部署中，ReCo在每种设置下均优于固定驻留调度、基于计数的调度以及使用相同指标的门限规则。其优势不能仅用收集更多天数来解释，而是反映了将天数分配给更有价值的家庭和时期。
英文摘要
Many sensing tasks obtain training labels only by deploying instruments in the field. With a limited number of sensor kits, a collection deadline, and measurement downtime at every move, the collector must repeatedly decide whether to stay at the current site or relocate. We study this decision in non-intrusive load monitoring (NILM), which estimates the power drawn by individual appliances from a home's main meter and is trained on data from homes temporarily fitted with appliance-level sub-meters. In NILM, appliance usage varies with the appliance, season and climate, and the value of new data depends on how diverse the combinations of target operation and background load are. To address this, we propose a constraint-based relocation framework and instantiate it for NILM as ReCo (Relocation by Coverage gain). ReCo counts new operating regimes in a joint target-background feature space, forecasts each home's future gain from the data collected so far, and each night weighs the gain of staying against the gain of moving elsewhere after the downtime. In replayed deployments on the Plegma dataset under two kit counts and two downtime costs, ReCo outperforms fixed-dwell and count-based schedules and a threshold rule using the same metric in every setting. Its advantage is not explained by collecting more days alone and reflects allocating the days to more valuable homes and periods.
发表机构
- McMaster University（麦克马斯特大学）
- Shanghai Eneintel Technology Co., Ltd.（上海亿恩特尔科技有限公司）
机构由 AI 辅助整理，请以论文原文为准。
