Knowledge on water-use patterns in residential settings can help policymakers formulate well-targeted water conservation measures and evaluate the efficacy of such measures. Prior studies have applied machine learning techniques to disaggregate household-level water consumption data, collected by smart meters, into specific end-use categories, such as showering, basin use, kitchen use, and use by washing machine. However, the analysis of the effect of the sampling interval on the classification accuracy level remains an underinvestigated issue. This article seeks to fill this knowledge gap by identifying an optimal sampling interval that can achieve a high level of classification accuracy while overcoming constraints of on-device data storage, data transmission, and energy consumption. To understand the benefits of collecting fine-grained data for machine learning, we have built a high-resolution tap-based Internet of Things (IoT) system comprising a set of Wi-Fi-based tap sensors, gateway infrastructure, and a secure data processing pipeline. Based on empirical tap-based data collected over an eight-month period, we concluded that when the sampling interval decreases slightly from 5 to 1 s, the accuracy level of the end-use classification model increases significantly from 66.6% to 76.1 %. This article also highlights the challenges of deploying IoT sensors to collect water consumption data in a domestic setting. In order to collect sufficient ground-truth data for the training and verification of a generalizable water end-use disaggregation model, it is necessary to sophisticate the flow data collection system by adopting low-power wide-area network technologies and reducing the level of energy consumption of the flow sensing components.

中文翻译：

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用于流量数据收集和水最终用途分析的高分辨率基于水龙头的物联网系统

了解住宅环境中的用水模式可以帮助政策制定者制定有针对性的节水措施并评估这些措施的有效性。先前的研究已经应用机器学习技术将智能仪表收集的家庭用水量数据分解为特定的最终用途类别，例如淋浴、盆地使用、厨房使用和洗衣机使用。然而，分析采样间隔对分类准确度水平的影响仍然是一个研究不足的问题。本文旨在通过确定一个最佳采样间隔来填补这一知识空白，该间隔可以实现高水平的分类准确度，同时克服设备上数据存储、数据传输和能源消耗的限制。为了了解为机器学习收集细粒度数据的好处，我们构建了一个高分辨率的基于 Tap 的物联网 (IoT) 系统，包括一组基于 Wi-Fi 的 Tap 传感器、网关基础设施和安全数据处理管道。根据在八个月期间收集的基于 Tap 的经验数据，我们得出结论，当采样间隔从 5 秒略微减小到 1 秒时，最终用途分类模型的准确度水平从 66.6% 显着提高到 76.1%。本文还强调了在家庭环境中部署物联网传感器来收集用水量数据的挑战。为了收集足够的真实数据用于可推广的水最终用途分解模型的训练和验证，