Dynamic Forecast of Daily Urban Water Consumption Using a Variable-Structure Support Vector Regression Model
Publication: Journal of Water Resources Planning and Management
Volume 141, Issue 3
Abstract
A reliable forecasting model for daily water consumption would provide the data basis for scheduling urban water supply facilities. In this paper, a variable-structure support vector regression (VS-SVR) model is developed for dynamic forecast of the water consumption. Considering the nonlinear mapping capability of the SVR, the next-day water consumption is associated with the past water consumption series using the SVR model. To better accommodate the dynamic characteristics, the model structure of the SVR is variable in response to the receding horizon of the water consumption series. The variable model structural parameters are obtained using an extended Kalman filter (EKF) as the feedback correction tool. Combining the robustness of the model predictive control framework and the nonlinearity of the SVR, the proposed VS-SVR model is a dynamic approach to forecasting daily urban water consumption, evaluated using real data collected from a water company from January 2010 to December 2011. Compared with the SVR model, the dynamic forecast of daily urban water consumption using the proposed VS-SVR method improves the one-day-ahead forecast mean absolute error by (1.2% mean absolute percentage error). The results show that the dynamic update is better, at least in a global sense.
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Acknowledgments
This work is supported in part by the Natural Science Foundation of China (71271226 51375517), the Natural Science Foundation of CQ CSTC (2012JJJQ70001), the Project of Chongqing Innovation Team in University (KJTD201313), the National Key Technology Research and Development Program of China (2012BAH32F01, 03), and the Ministry of Housing and Urban-Rural of China (2001-45). Their supports are gratefully acknowledged. The authors would also like to thank the editors/reviewers for their valuable suggestions and comments that have helped improve this paper.
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Published In
Journal of Water Resources Planning and Management
Volume 141 • Issue 3 • March 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Oct 29, 2013
Accepted: Apr 9, 2014
Published online: Jul 15, 2014
Discussion open until: Dec 15, 2014
Published in print: Mar 1, 2015
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