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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/44552


    Title: Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves
    Authors: Chang, Ya-ting;張麗秋;Chang, Li-chiu;Chang, Fi-john
    Contributors: 淡江大學水資源及環境工程學系
    Keywords: genetic algorithm;artificial neural network;fuzzy rule base;adaptive network-based fuzzy inference system;reservoir operation
    Date: 2005-04-30
    Issue Date: 2010-03-26 16:17:45 (UTC+8)
    Publisher: Bognor Regis: John Wiley & Sons Ltd.
    Abstract: To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network-based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M-5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M-5 curves in real-time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved.
    Relation: Hydrological processes 19(7), pp.1431-1444
    DOI: 10.1002/hyp.5582
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

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