Adaptive stochastic management of the storage function for a large, open reservoir using learned fuzzy models
Identifikátory výsledku
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>
Alternativní jazyky
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
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10501 - Hydrology
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
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