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Adaptive stochastic management of the storage function for a large, open reservoir using learned fuzzy models

Adaptive stochastic management of the storage function for a large, open reservoir using learned... AbstractThe design and evaluation of algorithms for adaptive stochastic control of the reservoir function of a water reservoir using an artificial intelligence method (learned fuzzy model) are described in this article. This procedure was tested on the Vranov reservoir (Czech Republic). Stochastic model results were compared with the results of deterministic management obtained using the method of classical optimisation (differential evolution). The models used for controlling of reservoir outflow used single quantile from flow duration curve values or combinations of quantile values from flow duration curve for determination of controlled outflow. Both methods were also tested on forecast data from real series (100% forecast). Finally, the results of the dispatcher graph, adaptive deterministic control and adaptive stochastic control were compared. Achieved results of adaptive stochastic management were better than results provided by dispatcher graph and provide inspiration for continuing research in the field. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrology and Hydromechanics de Gruyter

Adaptive stochastic management of the storage function for a large, open reservoir using learned fuzzy models

9 pages

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Publisher
de Gruyter
Copyright
© 2022 Tomas Kozel et al., published by Sciendo
ISSN
0042-790X
eISSN
1338-4333
DOI
10.2478/johh-2022-0010
Publisher site
See Article on Publisher Site

Abstract

AbstractThe design and evaluation of algorithms for adaptive stochastic control of the reservoir function of a water reservoir using an artificial intelligence method (learned fuzzy model) are described in this article. This procedure was tested on the Vranov reservoir (Czech Republic). Stochastic model results were compared with the results of deterministic management obtained using the method of classical optimisation (differential evolution). The models used for controlling of reservoir outflow used single quantile from flow duration curve values or combinations of quantile values from flow duration curve for determination of controlled outflow. Both methods were also tested on forecast data from real series (100% forecast). Finally, the results of the dispatcher graph, adaptive deterministic control and adaptive stochastic control were compared. Achieved results of adaptive stochastic management were better than results provided by dispatcher graph and provide inspiration for continuing research in the field.

Journal

Journal of Hydrology and Hydromechanicsde Gruyter

Published: Jun 1, 2022

Keywords: Stochastic; Artificial intelligence; Storage function; Optimisation

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