Code underlying: Balancing Operator’s Risk Averseness in Model Predictive Control for Real-time Reservoir Flood Control.

DOI:10.4121/9a6a0464-2981-470a-8d7a-48c7e7fff27d.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/9a6a0464-2981-470a-8d7a-48c7e7fff27d
Datacite citation style:
Solomatine, Dimitri; Edo Abraham; Koo, Ja-Ho; Jonoski, Andreja (2025): Code underlying: Balancing Operator’s Risk Averseness in Model Predictive Control for Real-time Reservoir Flood Control. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/9a6a0464-2981-470a-8d7a-48c7e7fff27d.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Delft University of Technology logo

Geolocation

Daecheong reservoir, South Korea
lat (N): 127.480833
lon (E): 36.4775

Licence

CC BY 4.0

Python codes for the parameterized dynamic MPC implementation.

Inflow data is collected from the Data portal managed by Korean government.

PDMPC main is the python file to run the PDMPC, and other files are for the function or class using in main.py.

Formulation.py contains only linear MPC formulation by Pyomo. Evaluator.py includes python functions to evaluate the linear MPC results.

History

  • 2025-02-05 first online, published, posted

Publisher

4TU.ResearchData

Format

.txt, .zip, .py

Organizations

IHE Delft, Department of Hydroinformatics and Socio-Technical Innovation
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management
Korea Water Resources Public Corporation (K-water)

DATA

Files (5)