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 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.”

(Citation: Zanutto D, Michalopoulos C, Chatzistefanou G-A, Vamvakeridou-Lyroudia L, Tsiami L, Glynis K, Samartzis P, Hermes L, Hinder F, Vaquet J, et al. – A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning – Engineering Proceedings 69(2024)1, art. no. 60 – DOI: 10.3390/engproc2024069060 – (Open Access))

(This article belongs to the Proceedings of The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024))