A Multivariate LSTM Model for Short-Term Water Demand Forecasting †

Abstract

Accurate water demand forecasting is crucial for the effective operation and management of water distribution networks. Predicting future water demand empowers utilities to optimally operate system components. Various data-driven methodologies have been proposed for water demand forecasting, including artificial neural networks and econometric models. Recently, Long Short-Term Memory (LSTM) was shown to be particularly relevant for this application. Nevertheless, few studies have utilized multivariate-LSTM (M-LSTM) models for water demand forecasting. This study introduces an M-LSTM model incorporating historical water demands, meteorological data, and social variables to forecast short-term water demand. The proposed M-LSTM model performance was tested by applying it to the ten district metered areas (DMAs) case study of the Battle of Water Demand Forecasting (BWDF). The results demonstrated the model’s ability to accurately predict the hourly water demand one week in advance. The mean absolute error of the predictions ranged between 0.5 and 2.2 l/s (2.8% to 12.9% of the average demand). The results also showed a strong correlation between the prediction error and the variability of the water demand data.