Demand Estimation with Automated Meter Reading in a Distribution Network
Publication: Journal of Water Resources Planning and Management
Volume 137, Issue 5
Abstract
Accurate estimation and prediction of the demand patterns of the customers of a water works would enable more accurate network hydraulic management. In this study, a probabilistic model to generate residential demand patterns for single-family and semidetached houses is formed. To form these pattern models, an automated meter reading technique has been utilized to gather the necessary information from a sample of residences, which are used to model the demand behavior in a way that is applicable to a much wider range of residences. A linear regression model was constructed to predict the measured average weekly consumption from the calculated average weekly consumption. The residences were clustered by their weekly water demand into four distinct classes using the -means algorithm. For the final result, probability models developed on the basis of mixtures of Gaussians for each class, in conjunction with the prediction model of the weekly water consumption, were utilized so that estimates for the demand pattern are obtained by sampling the probability distributions for individual single-family and semidetached houses.
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Acknowledgments
The Water Engineering Group of the Aalto University School of Science and Technology wishes to acknowledge the support given for this research by the Finnish Funding Agency for Technology and Innovation (Tekes), HS-Water, and all other participants in the project “The Real-Time Management of the Water Distribution Network.”
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Information
Published In
Journal of Water Resources Planning and Management
Volume 137 • Issue 5 • September 2011
Pages: 456 - 467
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Mar 29, 2010
Accepted: Oct 7, 2010
Published online: Oct 7, 2010
Published in print: Sep 1, 2011
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