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Sequential method of change detection and adaptive prediction of municipal water demand. (English) Zbl 0611.93059
An on-line recursive prediction algorithm for peak hourly flow rates of municipal water demands is presented. A simple state space model, which can be identified by using only the past hourly water flow rates time series, is proposed. Prediction accuracies are enhanced by means of sequential changes detection/acceptance decision rules and an adaptive filter, which revises the gain of the prediction algorithm to respond to the most recent process changes. A simulation test of the algorithm, using real historical water demand data from the city of Columbus, demonstrates the validity of the proposed algorithm.

MSC:
93E10 Estimation and detection in stochastic control theory
90B15 Stochastic network models in operations research
93E25 Computational methods in stochastic control (MSC2010)
62M20 Inference from stochastic processes and prediction
93C40 Adaptive control/observation systems
93E11 Filtering in stochastic control theory
Software:
SAS/ETS; SAS
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References:
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