A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning †
Dennis Zanutto (Politecnico di Milano, KWR Water Research Institute)
Christos Michalopoulos (KWR Water Research Institute, National Technical University of Athens)
Georgios Alexandros Chatzistefanou (University of Exeter, KWR Water Research Institute)
Lydia Vamvakeridou-Lyroudia (KWR Water Research Institute, University of Exeter)
Lydia Tsiami (National Technical University of Athens, KWR Water Research Institute)
Konstantinos Glynis (KWR Water Research Institute, TU Delft - Civil Engineering & Geosciences)
Panagiotis Samartzis (University of Macedonia)
Luca Hermes (Bielefeld University)
Fabian Hinder (Bielefeld University)
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Abstract
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.
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