Short-Term Water Demand Forecasting Based on LSTM Using Multi-Input Data

This study presents a forecasting framework for the hourly water demand of district metered areas (DMAs) based on a bidirectional long short-term memory (LSTM) model which is a participant in the Battle of Water Demand Forecasting (BWDF) during the 3rd WDSA/CCWI Joint Conference. The framework consists of three portions: raw data preprocessing, initial forecasting model establishment based on LSTM, and a correction based on weather and holiday factors. The application results in the DMAs provided by the BWDF show that the proposed framework demonstrates reasonable forecasting of water demands.