Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study

Forecasts of water use are crucial to efficiently manage water utilities to meet growing demand in urban areas. Improved household-level forecasts may be useful to water managers in order to accurately identify, and potentially target for management and conservation, low-efficiency homes and relative high-demand customers. Advanced machine learning (ML) techniques are available for feature-based predictions, but many of these methods ignore multiscale spatiotemporal associations that may improve prediction accuracy. We use a large dataset collected by Tampa Bay Water, a regional water wholesaler in southwest Florida, to evaluate an array of spatiotemporal statistical models and ML algorithms using out-of-sample prediction accuracy and uncertainty quantification to find the best tools for forecasting household-level monthly water demand. Time series models appear to provide the best short-term forecasts, indicating that the temporal dynamics of water use are more important for prediction than any exogenous features. •We evaluate the household-level monthly water use forecasts produced by a suite of statistical and machine learning models.•Traditional water use forecasting methods are improved upon using machine learning and spatio-temporal models.•Autoregressive and spatio-temporal models are shown to be highly accurate for one-month ahead forecasts.•Accurate forecasts produced by the methods studied may be used to identify and target inefficient homes and high-demand users.