Recent research highlights the potential of consumption-based feedback for water conservation, emphasizing the need for Non Intrusive Water Monitoring (NIWM). However, existing NIWM studies often rely on small datasets, a pre-selected class of models, and inaccessible software. Here, we introduce PyNIWM, a machine learning-based open-source Python framework for NIWM. PyNIWM enables water end-use classification via (i) data characterization and feature engineering, (ii) water end-use event classification with four machine learning classifiers, and (iii) performance assessment. We demonstrate PyNIWM on a real-world dataset containing around 800,000 labeled end-use events from 762 homes across the USA and Canada. The four PyNIWM classifiers achieve F1 scores above 0.85, indicating high suitability for water end-use classification. However, a tradeoff between accuracy and computational cost exists. Finally, data balancing through oversampling enhances classification of low-represented end-use classes, but does not improve overall classification. We release PyNIWM as an open-source software, aiming for collaborative and reproducible research.@en

A detailed characterization of residential water consumption is essential for ensuring urban water systems' capability to cope with changing water resources availability and water demands induced by growing population, urbanization, and climate change. Several studies have been conducted in the last decades to investigate the characteristics of residential water consumption with data at a sufficiently fine temporal resolution for grasping individual end uses of water. In this paper, we systematically review 114 studies to provide a comprehensive overview of the state-of-the-art research about water consumption at the end-use level. Specifically, we contribute with: (1) an in-depth discussion of the most relevant findings of each study, highlighting which water end-use characteristics were so far prioritized for investigation in different case studies and water demand modelling and management studies from around the world; and (2) a multi-level analysis to qualitatively and quantitatively compare the most common results available in the literature, i.e. daily per capita end-use water consumption, end-use parameter average values and statistical distributions, end-use daily profiles, end-use determinants, and considerations about efficiency and diffusion of water-saving end uses. Our findings can support water utilities, consumers, and researchers (1) in understanding which key aspects of water end uses were primarily investigated in the last decades; and (2) in exploring their main features considering different geographical, cultural, and socio-economic regions of the world.

@en