Smart Residential Water Monitoring: A Systematic Review of Sensing Technologies, Water End-Use Identification, and Artificial Intelligence

Residential water end-use monitoring is rapidly advancing from passive metering to AI-driven digital telemetry; however, systemic operational trade-offs remain poorly quantified. This systematic review synthesizes N = 100 peer-reviewed studies to critically evaluate algorithm architectures, sensing topologies, and deployment constraints in smart water disaggregation. Quantitative analysis reveals an overwhelming prevalence of non-intrusive paradigms (89.0%, N=89), with 45.0% (N=45) specifically targeting multi-fixture concurrent usage events. While high-frequency setups (≥1Hz) utilizing deep learning architectures (e.g., Seq2Point, 1D-CNNs) achieve high disaggregation accuracy (F1>0.88) in laboratory environments, performance degrades severely under commercial Automated Metering Infrastructure (AMI) supplying coarse 15–60min interval data. Four critical technical bottlenecks hindering utility-scale scalability are systematized: (1) severe temporal resolution mismatches between high-frequency training models and utility-scale AMI, (2) non-linear hydraulic superposition during simultaneous fixture activations, (3) domain transferability loss across heterogeneous plumbing topologies, and (4) memory and power constraints hindering edge TinyML execution (≤512KB SRAM). To address these challenges, an empirically validated three-layer conceptual analytics framework is integrated alongside five evidence-based future research directions, providing a structured roadmap to transition smart water analytics from experimental setups to robust, utility-scale deployments.