Sequential method of change detection and adaptive prediction of municipal water demand
作者:
TEP SASTRI,
期刊:
International Journal of Systems Science
(Taylor Available online 1987)
卷期:
Volume 18,
issue 6
页码: 1029-1049
ISSN:0020-7721
年代: 1987
DOI:10.1080/00207728708964030
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
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.
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