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Optimizing control of operations in a municipal water-distribution system can reduce electricity costs and realize other economic benefits. However, optimal control requires an ability to precisely predict short-term water demand so that minimum-cost pumping schedules can be prepared. One of the objectives of our project to develop an intelligent system for monitoring and controlling municipal water-supply systems is to ensure optimal control and reduce energy costs. Hence, prediction of water demand is essential. In this article, we present an application of a rough-set approach for automated discovery of rules from a set of data samples for daily water-demand predictions. The database contains 306 training samples, covering information on seven environmental and sociological factors and their corresponding daily volume of distribution flow.
Ning Shan, Nick Cercone, Christine Chan, Wojciech Ziarko, Aijun An, "Applying Knowledge Discovery to Predict Water-Supply Consumption", IEEE Intelligent Systems, vol. 12, no. , pp. 72-78, July-August 1997, doi:10.1109/64.608199
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