<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods (learning fuzzy model and neural networks) are described in this article. This procedure was tested on an artificial reservoir. Reservoir parameters have been designed to cause critical disturbances during the control process, and therefore the influences of control algorithms can be demonstrated in the course of controlled outflow of water from the reservoir. The results of the stochastic adaptive models were compared. Further, stochastic model results were compared with a resultant course of management obtained using the method of classical optimisation (differential evolution), which used stochastic forecast data from real series (100% forecast). Finally, the results of the dispatcher graph and adaptive stochastic control were compared. Achieved results of adaptive stochastic management provide inspiration for continuing research in the field.</p></abstract>

The design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods (learning fuzzy model and neural networks) are described in this article. This procedure was tested on an artificial reservoir. Reservoir parameters have been designed to cause critical disturbances during the control process, and therefore the influences of control algorithms can be demonstrated in the course of controlled outflow of water from the reservoir. The results of the stochastic adaptive models were compared. Further, stochastic model results were compared with a resultant course of management obtained using the method of classical optimisation (differential evolution), which used stochastic forecast data from real series (100% forecast). Finally, the results of the dispatcher graph and adaptive stochastic control were compared. Achieved results of adaptive stochastic management provide inspiration for continuing research in the field.

Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method

Faculty of Civil Engineering, Institute of Landscape Water Management

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods...

Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method

Online veröffentlicht: 15. Nov. 2019

Seitenbereich: 314 - 321

Eingereicht: 09. Nov. 2018

Akzeptiert: 09. Sept. 2019

DOI: https://doi.org/10.2478/johh-2019-0021

Schlüsselwörter
Stochastic, Artificial intelligence, Storage function, Optimisation

© 2019 Tomas Kozel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method

Online veröffentlicht: 15. Nov. 2019

Seitenbereich: 314 - 321

Eingereicht: 09. Nov. 2018

Akzeptiert: 09. Sept. 2019

DOI: https://doi.org/10.2478/johh-2019-0021

SchlüsselwörterStochastic, Artificial intelligence, Storage function, Optimisation

© 2019 Tomas Kozel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Stochastic, Artificial intelligence, Storage function, Optimisation