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

Nora Gourmelon,Christoph Sosna,Guy Bach,Michael Mayle,Christian Bebber,Siming Bayer,Christophe Munck,Andreas Maier

doi:10.3390/w13020236

Nora Gourmelon, Christoph Sosna + Show 6 more

Open Access

https://doi.org/10.3390/w13020236

Copy DOI

Journal: Water	Publication Date: Jan 19, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: University of Erlangen-Nuremberg, Diehl (Germany)

Abstract

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.

Highlights

With an increasing need for secured water supply, a better understanding of the water consumption behavior is beneficial
A clustering algorithm based on Dynamic Time Warping (DTW) as the similarity measure between events is described by Nguyen et al [2]
Figure presents the quantitative comparison of the accuracies for multi-class and Results binary Support Vector Machines (SVM)

Summary

Introduction

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. Both supervised and unsupervised machine learning (ML) techniques are employed, demonstrating accurate classification results on particular datasets. 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. The latter search for groups of similar data points in the dataset in the absence of labels Both unsupervised and supervised approaches are widely applied in studies on enduse classifications of water use events. Carranza et al [19] identified residential water end-uses in data measured by precision water meters equipped with pulse emitters using

Methods

Results

Discussion

Conclusion