A Non-Intrusive Water Consumption Monitoring System

• Main Authors: Somontina, James Adrian, Macabebe, Erees Queen B
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: fixtures; buildings; machine learning; research and development; water consumption; water monitoring; Random Forest; Internet-of-Things; fixture recognition; single- point sensing; Electrical and Computer Engineering; Water Resource Management
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/93; https://ieeexplore.ieee.org/document/9342898
• Institution: Ateneo De Manila University

Water is an essential resource for humans as it is used in many activities for both leisure and hygiene. However; the technology available in monitoring water consumption is limited to the traditional flowmeter. Households and small buildings rely only on the end-of-month billing by the water distributor. This study presents a water monitoring system that uses a pressure sensor which is a non-intrusive method of determining water activity. Aside from calculating the volume of water consumed; the system implements fixture recognition using machine learning as its main feature. This provides more information to users allowing them to identify appliances or fixtures that consume a lot of water. Multiple test sites were used with varying pipe networks from building restrooms to houses to see its viability. Results show that the fixture recognition; using preprocessing techniques; improved in performance with accuracy 88 %; precision of 91 %; recall at 88 %; leading to an f1-score of 87 %.