Understanding the residential end uses of water is helpful for the sustainable management of water resources and the implementation of water conservation strategies. In this study, over one hundred studies were systematically reviewed to provide a comprehensive overview of the state-of-the-art research on end-use water consumption. Each study was reviewed, clustered, and subjected to a multilevel analysis aimed at quantitatively comparing the characteristics of the end uses of water available in the literature. The findings of this work support water utilities, researchers, policy makers, and consumers in identifying the key aspects of water end uses and exploring their main features across different geographical, socioeconomic, and cultural regions of the world.

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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