10.15131/shef.data.5363884.v1
Juan Carlos Ibáñez Carranza
Juan Carlos Ibáñez
Carranza
Roberto Díaz Morales
Roberto Díaz
Morales
Jose Antonio Sánchez
Jose Antonio
Sánchez
CCWI2017: F14 'Pattern Recognition in Residential End Uses of Water Using Artificial Neural Networks and Other Machine-Learning Techniques'
The University of Sheffield
2017
Water End Use
Residential Water Use
Artificial Neural Networks
Support Vector Machines
Machine Learning
CCWI2017
Civil Engineering not elsewhere classified
2017-09-01 15:03:04
Journal contribution
https://orda.shef.ac.uk/articles/journal_contribution/CCWI2017_F14_Pattern_Recognition_in_Residential_End_Uses_of_Water_Using_Artificial_Neural_Networks_and_Other_Machine-Learning_Techniques_/5363884
Machine-Learning and other Artificial Intelligence techniques have nowadays many practical applications in engineering, science or everyday life. In the water industry, there is also a broad scope of potential applications. In this paper, it will be presented a system developed by Canal de Isabel II to identify residential use of water in its different appliances, based on records from precision water meters equipped with pulse emitters. Developed models are based on Support Vector Machines, and Artificial Neural Network paradigms. Training data sets for the models have been extracted from a sample of about 300 residential users in the Region of Madrid (Spain), monitored since 2008. In this time, more than 35 million of water use events have been registered and about 15 million hours of water consumption monitored. Machine-Learning techniques have proved to be an accurate and suitable method for automate this task that otherwise should require a huge number of man-hours of processing by operators.<br>