Work

Multi-Model Demand Forecasting in Water Distribution Network Districts

Year: 2024

Type: conference-paper

Abstract: A multi-model including three modelling elements is developed to solve the Battle of Water Demand Forecasting problem. The first two modelling elements working in parallel are a pattern-based algorithm and a Random Forest model. By varying the algorithm setting and predictors, 42 algorithms are constructed and calibrated using demand and weather data in the previous weeks to the generic n-th week, when the objective is the prediction of the hourly demand pattern in the n + 1-th week. Then, a third modelling element is used, which consists of an optimizer aimed at combining the results yielded by the 42 algorithms by analyzing algorithm performance in the n-th week. The same algorithm combination is used to forecast demand at the n + 1-th week.

DOI: 10.3390/engproc2024069188

Authors: Enrico Creaco, Carlo Giudicianni, Manuel Herrera

Institutions: University of Pavia, University of Cambridge

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Funders: Dipartimenti di Eccellenza, Ministero dell’Istruzione, dell’Università e della Ricerca, Università degli Studi di Pavia

Awards: G1234657840, G1303340219