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Water 2018, 10(9), 1221; https://doi.org/10.3390/w10091221

Enhancing Residential Water End Use Pattern Recognition Accuracy Using Self-Organizing Maps and K-Means Clustering Techniques: Autoflow v3.1

1
School of Engineering and Built Environment, Griffith University, Queensland 4222, Australia
2
Cities Research Institute, Griffith University, Queensland 4222, Australia
*
Author to whom correspondence should be addressed.
Received: 30 July 2018 / Revised: 29 August 2018 / Accepted: 6 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue Smart Technologies and Water Supply Planning)
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Abstract

The aim of residential water end-use studies is to disaggregate water consumption into different water end-use categories (i.e., shower, toilet, etc.). The authors previously developed a beta application software (i.e., Autoflow v2.1) that provides an intelligent platform to autonomously categorize residential water consumption data and generate management analysis reports. However, the Autoflow v2.1 software water end use event recognition accuracy achieved was between 75 to 90%, which leaves room for improvement. In the present study, a new module augmented to the existing procedure improved flow disaggregation accuracy, which resulted in Autoflow v3.1. The new module applied self-organizing maps (SOM) and K-means clustering algorithms for undertaking an initial pre-grouping of water end-use events before the existing pattern recognition procedures were applied (i.e., ANN, HMM, etc.) For validation, a dataset consisting of over 100,000 events from 252 homes in Australia were employed to verify accuracy improvements derived from augmenting the new hybrid SOM and K-means algorithm techniques into the existing Autoflow v2.1 software. The water end use event categorization accuracy ranged from 86 to 94.2% for the enhanced model (Autoflow v3.1), which was a 1.7 to 9% improvement on event categorization. View Full-Text
Keywords: water end-use; K-means clustering; self-organizing maps; Autoflow; water consumption water end-use; K-means clustering; self-organizing maps; Autoflow; water consumption
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Yang, A.; Zhang, H.; Stewart, R.A.; Nguyen, K. Enhancing Residential Water End Use Pattern Recognition Accuracy Using Self-Organizing Maps and K-Means Clustering Techniques: Autoflow v3.1. Water 2018, 10, 1221.

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