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Open AccessArticle

Implications of Experiment Set-Ups for Residential Water End-Use Classification

1
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstraße 3, 91058 Erlangen, Germany
2
Diehl Metering GmbH, Industriestraße 13, 91522 Ansbach, Germany
3
Diehl Metering GmbH, Donaustraße 120, 90451 Nuremberg, Germany
4
Diehl Metering SAS, 67 Rue du Rhone, 68300 Saint-Louis, France
*
Authors to whom correspondence should be addressed.
Water 2021, 13(2), 236; https://doi.org/10.3390/w13020236
Received: 21 December 2020 / Revised: 14 January 2021 / Accepted: 15 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Water and the Ecosphere in the Anthropocene)
With an increasing need for secured water supply, a better understanding of the water consumption behavior is beneficial. This can be achieved through end-use classification, i.e., identifying end-uses such as toilets, showers or dishwashers from water consumption data. Previously, both supervised and unsupervised machine learning (ML) techniques are employed, demonstrating accurate classification results on particular datasets. However, a comprehensive comparison of ML techniques on a common dataset is still missing. Hence, in this study, we are aiming at a quantitative evaluation of various ML techniques on a common dataset. For this purpose, a stochastic water consumption simulation tool with high capability to model the real-world water consumption pattern is applied to generate residential data. Subsequently, unsupervised clustering methods, such as dynamic time warping, k-means, DBSCAN, OPTICS and Hough transform, are compared to supervised methods based on SVM. The quantitative results demonstrate that supervised approaches are capable to classify common residential end-uses (toilet, shower, faucet, dishwasher, washing machine, bathtub and mixed water-uses) with accuracies up to 0.99, whereas unsupervised methods fail to detect those consumption categories. In conclusion, clustering techniques alone are not suitable to separate end-use categories fully automatically. Hence, accurate labels are essential for the end-use classification of water events, where crowdsourcing and citizen science approaches pose feasible solutions for this purpose. View Full-Text
Keywords: end-use classification; smart water meter; machine learning; residential water end-use classification; smart water meter; machine learning; residential water
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MDPI and ACS Style

Gourmelon, N.; Bayer, S.; Mayle, M.; Bach, G.; Bebber, C.; Munck, C.; Sosna, C.; Maier, A. Implications of Experiment Set-Ups for Residential Water End-Use Classification. Water 2021, 13, 236. https://doi.org/10.3390/w13020236

AMA Style

Gourmelon N, Bayer S, Mayle M, Bach G, Bebber C, Munck C, Sosna C, Maier A. Implications of Experiment Set-Ups for Residential Water End-Use Classification. Water. 2021; 13(2):236. https://doi.org/10.3390/w13020236

Chicago/Turabian Style

Gourmelon, Nora; Bayer, Siming; Mayle, Michael; Bach, Guy; Bebber, Christian; Munck, Christophe; Sosna, Christoph; Maier, Andreas. 2021. "Implications of Experiment Set-Ups for Residential Water End-Use Classification" Water 13, no. 2: 236. https://doi.org/10.3390/w13020236

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