Adaptive Forecasting of Hourly Municipal Water Consumption
Publication: Journal of Water Resources Planning and Management
Volume 120, Issue 6
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.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 120 • Issue 6 • November 1994
Pages: 888 - 905
Copyright
Copyright © 1994 American Society of Civil Engineers.
History
Received: Jun 10, 1993
Published online: Nov 1, 1994
Published in print: Nov 1994
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