用交互式工具识别电表数据中的家电使用模式
DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series
- 基于类激活图的弱监督方法，仅需知道家电是否存在即可训练
- 能定位家庭用电中单个电器的使用时段，无需精细标注
- 适合电力公司或普通用户分析节能习惯，无需专业背景
近年来，全球已部署数百万智能电表以优化智能电网管理。这些设备采集大量用电数据，可帮助用户减少碳足迹，但非专业人士（如消费者或销售顾问）难以从中理解各电器的使用模式，因数据反映的是所有电器的聚合行为。同时，真实标签（用于训练电器检测与定位模型）成本高昂且极度稀缺。本文提出DeviceScope，一种交互式工具，通过CamAL（基于类激活图的电器定位）方法，在仅知某电器存在于家庭的前提下，实现对特定时间段内电器使用模式的检测与定位。该系统助力非专家用户更直观地理解用电数据。论文发表于ICDE 2025。
原文摘要 · Abstract (English)
In recent years, electricity suppliers have installed millions of smart meters worldwide to improve the management of the smart grid system. These meters collect a large amount of electrical consumption data to produce valuable information to help consumers reduce their electricity footprint. However, having non-expert users (e.g., consumers or sales advisors) understand these data and derive usage patterns for different appliances has become a significant challenge for electricity suppliers because these data record the aggregated behavior of all appliances. At the same time, ground-truth labels (which could train appliance detection and localization models) are expensive to collect and extremely scarce in practice. This paper introduces DeviceScope, an interactive tool designed to facilitate understanding smart meter data by detecting and localizing individual appliance patterns within a given time period. Our system is based on CamAL (Class Activation Map-based Appliance Localization), a novel weakly supervised approach for appliance localization that only requires the knowledge of the existence of an appliance in a household to be trained. This paper appeared in ICDE 2025.
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