Short-Term Bathwater Demand Forecasting for Shared Shower Rooms in Smart Campuses Using Machine Learning Methods

Abstract

Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts.