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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...

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Detalles Bibliográficos
Autores principales: Akbarifard, Saeid, Sharifi, Mohammad Reza, Qaderi, Kourosh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
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.
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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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