A short-term water demand forecasting model using multivariate long short-term memory with meteorological data

Abstract

Sustainable management of water resources is a key challenge nowadays and in the future. Water distribution systems have to ensure fresh water for all users in an increasing demand scenario related to the long-term effects due to climate change. In this context, a reliable shortterm water demand forecasting model is crucial for the optimal management of water resources. This study proposes a novel deep learning model based on long short-term memory (LSTM) neural networks to forecast hourly water demand. Due to the limitations of using multiple input sequences with different time lengths using LSTM, the proposed deep learning model is developed with two modules that process different temporal sequences of data: a first module aimed at dealing with short-term meteorological information and a second module aimed at representing the longer-term information of the water demand. The proposed dual-module structure allows a multivariate selection of the inputs with sequences of a different time length. The performance of the proposed deep learning model is compared to a conventional multi-layer perceptron (MLP) and a seasonal integrated moving average (SARIMA) model in a real case study. The results highlight the potential of the proposed multivariate approach in short-term water demand prediction, outperforming the more conventional approaches.