Gourmelon N, Bayer S, Mayle M, Bach G, Bebber C, Munck C, Sosna C, Maier A (2021)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2021
Book Volume: 13
Article Number: 236
Journal Issue: 2
URI: https://www.mdpi.com/2073-4441/13/2/236
DOI: 10.3390/w13020236
Open Access Link: https://www.mdpi.com/2073-4441/13/2/236
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.
APA:
Gourmelon, N., Bayer, S., Mayle, M., Bach, G., Bebber, C., Munck, C.,... Maier, A. (2021). Implications of Experiment Set-Ups for Residential Water End-Use Classification. Water, 13(2). https://dx.doi.org/10.3390/w13020236
MLA:
Gourmelon, Nora, et al. "Implications of Experiment Set-Ups for Residential Water End-Use Classification." Water 13.2 (2021).
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