| Issue |
MATEC Web Conf.
Volume 295, 2019
Smart Underground Space and Infrastructures – Lille 2019
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 5 | |
| Section | Smart Underground Space and Infrastructures | |
| DOI | https://doi.org/10.1051/matecconf/201929501004 | |
| Published online | 18 October 2019 | |
Prediction of water consumption using Artificial Neural Networks modelling (ANN)
1 Laboratoire de Génie Civil et géo-Environnement, Université de Lille, 5900 Lille, France
2 Civil Engineering Department, Lebanese University, Faculty of Engineering, Branch 1, North Campus, Ras Maska, Koura, Lebanon
3 Civil Engineering Department, Holy Spirit University of Kaslik USEK, Faculty of Engineering, Jounieh, Lebanon
4 Faculty of Technology, Lebanese University, Saida, Lebanon
* Corresponding author: elias_farah@live.com
This paper presents an application of Artificial Neural Network models (ANN) to predict the water consumption at two scales: i) District Metered Area (DMA) located in the Scientific Campus of Lille University and ii) End user representing a restaurant inside this DMA. Data are collected from Automated Meter Readers (AMRs) that measure in near real-time the water consumption. The models are trained at both daily and hourly time intervals using historical values and the variation between the hour and the type of days. The paper shows that the ANN-based models can well predict the water consumption including peak values.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.