Authors:
Sarah Di Grande
1
;
Mariaelena Berlotti
1
;
Salvatore Cavalieri
1
and
Roberto Gueli
2
Affiliations:
1
Department of Electrical Electronic and Computer Engineering, University of Catania, Viale A. Doria n.6, Catania, Italy
;
2
EHT, Viale Africa n.31, Catania, Italy
Keyword(s):
Water Distribution System, Water 4.0, Water Demand Forecasting, Energy Consumption Forecasting, Machine Learning.
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.
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