<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The 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.</p>
</abstract>

The 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.

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

Brno University of Technology, Faculty of Civil Engineering

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

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

Pubblicato online: 19 mag 2022

Pagine: 213 - 221

Ricevuto: 23 lug 2021

Accettato: 10 feb 2022

DOI: https://doi.org/10.2478/johh-2022-0010

Parole chiave
Stochastic, Artificial intelligence, Storage function, Optimisation

© 2022 Tomas Kozel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Pubblicato online: 19 mag 2022

Pagine: 213 - 221

Ricevuto: 23 lug 2021

Accettato: 10 feb 2022

DOI: https://doi.org/10.2478/johh-2022-0010

Parole chiaveStochastic, Artificial intelligence, Storage function, Optimisation

© 2022 Tomas Kozel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Stochastic, Artificial intelligence, Storage function, Optimisation