A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

This study presents a collaborative framework developed by the Water Futures team of researchers for the “Battle of the Water Demand Forecasting” challenge at the 3rd International WDSA-CCWI Joint Conference. The framework integrates an ensemble of machine learning forecasting models into a determin...

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Main Authors: Dennis Zanutto, Christos Michalopoulos, Georgios-Alexandros Chatzistefanou, Lydia Vamvakeridou-Lyroudia, Lydia Tsiami, Konstantinos Glynis, Panagiotis Samartzis, Luca Hermes, Fabian Hinder, Jonas Vaquet, Valerie Vaquet, Demetrios Eliades, Marios Polycarpou, Phoebe Koundouri, Barbara Hammer, Dragan Savić
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Engineering Proceedings
Subjects:
machine learning
ensemble forecasting
water demand forecasting
district metered areas
deep learning
Online Access:https://www.mdpi.com/2673-4591/69/1/60
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Summary:This study presents a collaborative framework developed by the Water Futures team of researchers for the “Battle of the Water Demand Forecasting” challenge at the 3rd International WDSA-CCWI Joint Conference. The framework integrates an ensemble of machine learning forecasting models into a deterministic outcome consistent with the competition formulation. The water demand trajectory over a week exhibits complex overlapping patterns and non-linear dependencies to multiple features and time-dependent events that a single model cannot accurately predict. As such, the reconciled forecast from an ensemble of models exceeds the performance of the individual ones and exhibits higher stability across the weeks of the year and district metered areas considered.
ISSN:2673-4591

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