Detail publikačního výsledku

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

KOZEL, T.; STARÝ, M.

Original Title

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

English Title

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

Type

WoS Article

Original 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.

English 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.

Keywords

Stochastic; Artificial intelligence; Storage function; Optimisation

Key words in English

Stochastic; Artificial intelligence; Storage function; Optimisation

Authors

KOZEL, T.; STARÝ, M.

RIV year

2020

Released

15.12.2019

Publisher

Journal of Hydrology and Hydromechanics

Location

Bratislava

ISBN

0042-790X

Periodical

Journal of Hydrology and Hydromechanics

Volume

64

Number

4

State

Slovak Republic

Pages from

314

Pages to

321

Pages count

8

URL

http://www.uh.sav.sk/Portals/16/vc_articles/2019_67_4_Kozel_314.pdf

Full text in the Digital Library

http://hdl.handle.net/11012/200894

BibTex

@article{BUT161073,
  author="Tomáš {Kozel} and Miloš {Starý}",
  title="Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method",
  journal="Journal of Hydrology and Hydromechanics",
  year="2019",
  volume="64",
  number="4",
  pages="314--321",
  doi="10.2478/johh-2019-0021",
  issn="0042-790X",
  url="http://www.uh.sav.sk/Portals/16/vc_articles/2019_67_4_Kozel_314.pdf"
}

Documents

10.2478_johh-2019-0021