Year: 2024
Type: conference-paper
Abstract: Timeseries forecasting holds a prominent position in the domain of urban water systems. Most forecasting approaches are designed to provide single-point deterministic forecasts, neglecting the uncertainty in model predictions. In this work, we propose a methodological framework, able to provide probabilistic predictions over lead times of operational interest, by combining machine learning (ML) methods with multivariate statistics (i.e., copulas). The idea is that ML methods can be used to provide deterministic forecasts, and copulas can be used to quantify the predictive uncertainty of the forecasts. We showcase the effectiveness of proposed framework using hourly water demand data from a real-world case study.
Authors: Panagiotis Kossieris, Ioannis Tsoukalas, Dionysios Nikolopoulos, Georgios Moraitis, Christos Makropoulos
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402418934