Abstract—The smart water meter collected data has made a
great progress for the categorization of residential water end
use events, the efficiency and accuracy still need to be improved.
In this paper, an advanced algorithm is proposed for clustering
the end-use category of a mechanical appliance. For this study,
the database of end use events was collected using smart meters
from over 200 households located in South-east Queensland
(SEQ), Australia. Firstly, the raw data is pre-processed and
physical characteristics (e.g., volume, duration, max flowrate,
etc.) are extracted. Due to the type of the dataset is water end
used flow data, which based on time series, a K-Medoids
clustering algorithm based on the Dynamic Time Warping
algorithm is used for clustering. In addition, a swarm
intelligence which is named Artificial Bee Colony algorithm
brings the whole system into equilibrium. Numerical
experiments are based on toilet flushing events. Results indicate
that the hybrid technique improves the clustering accuracy
from 82.85% to 95.71%, and it can be implemented to other
mechanical water end use events such as clothes washers and
Index Terms—Artificial bee colony algorithm, dynamic time warping algorithm, water end-use, K-Mediods clustering.
A. Yang, H. Zhang, and R. A. Stewart are with the Griffith School of Engineering, Griffith University, QLD 4222, Australia (e-mail: ao.yang@ griffithuni.edu.au, email@example.com, firstname.lastname@example.org).
K. A. Nguyen is with the Cities Research Institute, Griffith University, QLD 4222, Australia (e-mail: email@example.com).
Cite: A. Yang, H. Zhang, R. A. Stewart, and K. A. Nguyen, "Water End Use Clustering Using Hybrid Pattern Recognition Techniques — Artificial Bee Colony, Dynamic Time Warping and K-Medoids Clustering," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 483-487, 2018.