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Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms
This article describes the time series data for optimizing the hydropower operation of the Karun-4 reservoir located in Iran for a period of 106 months (from October 2010 to July 2019). The utilized time-series data included reservoir inflow, reservoir storage, evaporation from the reservoir, precip...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6965708/ https://www.ncbi.nlm.nih.gov/pubmed/31970276 http://dx.doi.org/10.1016/j.dib.2019.105048 |
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author | Akbarifard, Saeid Sharifi, Mohammad Reza Qaderi, Kourosh |
author_facet | Akbarifard, Saeid Sharifi, Mohammad Reza Qaderi, Kourosh |
author_sort | Akbarifard, Saeid |
collection | PubMed |
description | This article describes the time series data for optimizing the hydropower operation of the Karun-4 reservoir located in Iran for a period of 106 months (from October 2010 to July 2019). The utilized time-series data included reservoir inflow, reservoir storage, evaporation from the reservoir, precipitation on the reservoir, and release of water through the power plant. In this data article, a model based on Moth Swarm Algorithm (MSA) was developed for the optimization of water resources. The analysis showed that the best solutions achieved by the MSA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were 0.147, 0.3026, and 0.1584, respectively. The analysis of these datasets revealed that the MSA algorithm was superior to GA and PSO algorithms in the optimal operation of the hydropower reservoir problem. |
format | Online Article Text |
id | pubmed-6965708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-69657082020-01-22 Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms Akbarifard, Saeid Sharifi, Mohammad Reza Qaderi, Kourosh Data Brief Computer Science This article describes the time series data for optimizing the hydropower operation of the Karun-4 reservoir located in Iran for a period of 106 months (from October 2010 to July 2019). The utilized time-series data included reservoir inflow, reservoir storage, evaporation from the reservoir, precipitation on the reservoir, and release of water through the power plant. In this data article, a model based on Moth Swarm Algorithm (MSA) was developed for the optimization of water resources. The analysis showed that the best solutions achieved by the MSA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were 0.147, 0.3026, and 0.1584, respectively. The analysis of these datasets revealed that the MSA algorithm was superior to GA and PSO algorithms in the optimal operation of the hydropower reservoir problem. Elsevier 2020-01-08 /pmc/articles/PMC6965708/ /pubmed/31970276 http://dx.doi.org/10.1016/j.dib.2019.105048 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Computer Science Akbarifard, Saeid Sharifi, Mohammad Reza Qaderi, Kourosh Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms |
title | Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms |
title_full | Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms |
title_fullStr | Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms |
title_full_unstemmed | Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms |
title_short | Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms |
title_sort | data on optimization of the karun-4 hydropower reservoir operation using evolutionary algorithms |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6965708/ https://www.ncbi.nlm.nih.gov/pubmed/31970276 http://dx.doi.org/10.1016/j.dib.2019.105048 |
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