Adaptive Forecasting of Hourly Municipal Water Consumption

Abstract

An adaptive smoothing‐filtering approach for on‐line forecasting of hourly municipal water use time series is presented. This method is suitable for forecasting an hourly water‐consumption time series that is influenced by changing weather conditions and measurement outliers. The proposed seasonal time‐series model and adaptive forecasting algorithm can capture both weekday and weekend cycles and produce very accurate forecasts from 1 h to 24 h ahead. The methodology is based on Winters' exponential smoothing, recursive least squares (RLS), and the Kalman filter. The Winters algorithm is useful for recursive updating and extracting time‐varying seasonal factors. The deseasonalized residuals are passed on to the RLS and the filter to correct model errors and to whiten the innovations. The on‐line adaptive forecasting system also utilizes a data preprocessing procedure to handle measurement outliers, which are caused by data‐recording errors and unmodeled disturbances. The validation tests conducted in the present study show that the forecasting system can maintain surprisingly small prediction errors, despite various unmodeled time‐varying climatic variabilities.