Carboni Davide, Gluhak Alex, McCann Julie A, Beach Thomas H
ICRI Sustainable Connected Cities, Intel Corp., London SW7 2AZ, UK.
Digital Catapult, London NW1 2RA, UK.
Sensors (Basel). 2016 May 20;16(5):738. doi: 10.3390/s16050738.
Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included.
家庭用水监测对于确保地球上淡水资源的可持续性至关重要。它为利益相关者提供了制定住宅用水管理最优策略所需的统计数据。然而，这不应具有过高成本，而且由于硬件成本较高和复杂性，在每个感兴趣的水龙头或用水设备上部署传感器实际上无法实现设备级别的用水监测，更不用说额外管道所需带来的意外泄漏风险了。机器学习和数据挖掘技术是分析监测数据以获得非侵入式用水分解的有前景的技术。这是因为它们可以从从单个观测点获取的聚合数据中辨别用水情况。本文概述了用于水事件分类的用水分解系统及相关技术。回顾了用于装置识别的算法和测试平台的最新技术，并对突出挑战和未来研究进行了讨论。
