Urban Water Demand Forecasting Using DeepAR-Models as Part of the Battle of Water Demand Forecasting (BWDF) †

Abstract

The accurate and reliable short-term forecasting of urban water demand plays a crucial role in enabling drinking water utilities to operate sustainably and secure water supplies in the future. Here, we apply state-of-the-art DeepAR models to predict urban water demand in ten district metered areas (DMAs) in a water distribution system in northeastern Italy. DeepAR models are based on long short-term memory networks and can directly provide probabilistic results. For this contribution, we leverage past flow data, current and future weather data, and engineered weather and date features as input to predict flow data one week ahead. A local model for each DMA is prepared and applied after hyperparameter optimization.