Water Demand Forecast Using Generalized Autoregressive Moving Average Models
Year: 2024
Type: conference-paper
Abstract: Short-time forecasting of the demand on water distribution networks is a challenging task because of the high variability and uncertainty of that demand. Of the different approaches used, we consider the probability modeling of demand time series to be the most interesting, and specifically propose the use of Generalized Autoregressive Moving Average (GARMA) models. The complete proposed model uses a gamma probability density function, variables for weekends, and harmonic functions for daily and weekly seasonality, among other parameters. In the context of the Battle of Water Demand Forecasting, we train and test the model with a demand database for ten District Metered Areas. We obtain high accuracy, with mean absolute error values of around 0.25 L/s to 1.89 L/s.
Institution: Universidade de São Paulo
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402492514