A Proactive Approach for the Sustainable Management of Water Distribution Systems

Sarah Di Grande, Mariaelena Berlotti, Salvatore Cavalieri, Roberto Gueli

2023

Abstract

Today, water distribution systems need to supply water to consumers in a sustainable way. This is connected to the concept of Watergy, which means the satisfaction of user demand with the least possible use of water and energy resources. Thanks to modern technologies, the forecasting of water and energy demand can help achieve this goal. In particular, water demand forecasting allows water distribution companies to know in advance how water resources will be allocated, it can help identify any anomalies in water consumption, and it is essential for pumps scheduling. On the other hand, energy consumption forecasting has other important roles, such as energy optimization, identification of anomalous consumption, and planning of energy load. The present paper aims to develop short-term water demand and energy forecasting models through innovative machine learning-based methodologies for the water distribution sector: global forecasting models, the N-Beats machine learning algorithm, and transfer learning approaches. These tools demonstrated very good performances in the creation of the models previously mentioned.

Paper Citation

in Harvard Style

Di Grande S., Berlotti M., Cavalieri S. and Gueli R. (2023). A Proactive Approach for the Sustainable Management of Water Distribution Systems. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 115-125. DOI: 10.5220/0012121200003541

in Bibtex Style

@conference{data23,
author={Sarah Di Grande and Mariaelena Berlotti and Salvatore Cavalieri and Roberto Gueli},
title={A Proactive Approach for the Sustainable Management of Water Distribution Systems},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={115-125},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012121200003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - A Proactive Approach for the Sustainable Management of Water Distribution Systems
SN - 978-989-758-664-4
AU - Di Grande S.
AU - Berlotti M.
AU - Cavalieri S.
AU - Gueli R.
PY - 2023
SP - 115
EP - 125
DO - 10.5220/0012121200003541
PB - SciTePress