Real-Time Data Assimilation for Improving Linear Municipal Solid Waste Prediction Model: A Case Study in Seattle
Publication: Journal of Energy Engineering
Volume 141, Issue 4
Abstract
A commonly used data assimilation (DA) algorithm, Kalman filter, is integrated with the seasonal autoregressive integrated moving average (SARIMA) model to make a one-step forecast of monthly municipal solid waste (MSW) generation in Seattle. The DA solves the problem that parameters of the forecasting model need to be updated in every forecasting process. The performances of prediction models are compared using mean absolute percentage error (MAPE), root-mean-square-error (RMSE), and 95% confidence interval. The MAPE of the SARIMA model with DA is 0.0422, whereas the MAPE of the SARIMA without DA is 0.0914. A 95% confidence interval of SARIMA without DA keeps increasing, whereas SARIMA with DA remains constant, which means DA raises the stability of SARIMA as time progresses. Results show that DA enables the same MSW prediction model with more accurate and more robust forecast results. The SARIMA parameter updating cycle can be prolonged, which saves time and effort.
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Acknowledgments
The authors would like to thank Seattle Public Utilities for providing the monthly MSW data of Seattle from January 1996 to June 2013. This article was supported by the program of International S&T Cooperation “Fined Earth Observation and Recognition of the Impact of Global Change on World Heritage Sites” (Grant No. S2013GR0477).
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Published In
Journal of Energy Engineering
Volume 141 • Issue 4 • December 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: May 7, 2014
Accepted: Jun 18, 2014
Published online: Aug 4, 2014
Discussion open until: Jan 4, 2015
Published in print: Dec 1, 2015
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