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This Data supports the University of Southampton Doctoral Thesis "Robust optimization technique for hydropower optimization".

This Data supports the University of Southampton Doctoral Thesis "Robust optimization technique for hydropower optimization".
This Data supports the University of Southampton Doctoral Thesis "Robust optimization technique for hydropower optimization".
This data supports the University of Southampton Doctoral thesis "Robust Optimization Technique for Hydropower Optimization" by J Badrodin. The data are available in the literature (Anghileri, Castelletti et al., 2018) and on the website https://www.kraftwerkemattmarkag.ch/anlagen/. We made reasonable assumptions whenever information was not publicly available. The inflow scenarios used in the training and validation set were stochastically generated using the model published in Anghileri, Castelletti et al., 2018.
University of Southampton
Badrodin, Jamaliatul Badriyah
9d3aa5e7-8e98-4659-9028-c1ff7495f3b1
Anghileri, Daniela
611ecf6c-55d5-4e63-b051-53e2324a7698
Badrodin, Jamaliatul Badriyah
9d3aa5e7-8e98-4659-9028-c1ff7495f3b1
Anghileri, Daniela
611ecf6c-55d5-4e63-b051-53e2324a7698

Badrodin, Jamaliatul Badriyah (2023) This Data supports the University of Southampton Doctoral Thesis "Robust optimization technique for hydropower optimization". University of Southampton doi:10.5258/SOTON/D2598 [Dataset]

Record type: Dataset

Abstract

This data supports the University of Southampton Doctoral thesis "Robust Optimization Technique for Hydropower Optimization" by J Badrodin. The data are available in the literature (Anghileri, Castelletti et al., 2018) and on the website https://www.kraftwerkemattmarkag.ch/anlagen/. We made reasonable assumptions whenever information was not publicly available. The inflow scenarios used in the training and validation set were stochastically generated using the model published in Anghileri, Castelletti et al., 2018.

Spreadsheet
robust_hydropower_data.xlsx - Dataset
Available under License Creative Commons Attribution.
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Text
readme.txt - Text
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More information

Published date: 2023
Related URLs:
Learn more about School of Mathematical Sciences research

Identifiers

Local EPrints ID: 476753
URI: http://eprints.soton.ac.uk/id/eprint/476753
DOI: doi:10.5258/SOTON/D2598
PURE UUID: de6c0004-3c47-4043-890f-c1bfb46f6c28
ORCID for Jamaliatul Badriyah Badrodin: ORCID iD orcid.org/0000-0001-9093-5931
ORCID for Daniela Anghileri: ORCID iD orcid.org/0000-0001-6220-8593

Catalogue record

Date deposited: 12 May 2023 17:08
Last modified: 16 May 2023 01:52

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Contributors

Creator: Jamaliatul Badriyah Badrodin ORCID iD
Contributor: Daniela Anghileri ORCID iD

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