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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F22%3APU145097" target="_blank" >RIV/00216305:26110/22:PU145097 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciendo.com/article/10.2478/johh-2022-0010" target="_blank" >https://www.sciendo.com/article/10.2478/johh-2022-0010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/johh-2022-0010" target="_blank" >10.2478/johh-2022-0010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10501 - Hydrology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Hydrology and Hydromechanics

ISSN

0042-790X

e-ISSN

1338-4333

Svazek periodika

70

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

9

Strana od-do

213-221

Kód UT WoS článku

000797305300006

EID výsledku v databázi Scopus

2-s2.0-85131138728