TY - JOUR
T1 - A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring
AU - Gross, Marie-Philine
AU - Taormina, Riccardo
AU - Cominola, Andrea
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Machine learning
KW - Non Intrusive Water Monitoring
KW - Water end-use classification
UR - http://www.scopus.com/inward/record.url?scp=85207863271&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2024.106247
DO - 10.1016/j.envsoft.2024.106247
M3 - Article
AN - SCOPUS:85207863271
SN - 1364-8152
VL - 183
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 106247
ER -