Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms

Saeid Akbarifard; Mohammad Reza Sharifi; Kourosh Qaderi

doi:10.1016/j.dib.2019.105048

Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms

Data Brief. 2020 Jan 8:29:105048. doi: 10.1016/j.dib.2019.105048. eCollection 2020 Apr.

Authors

Saeid Akbarifard¹, Mohammad Reza Sharifi², Kourosh Qaderi³

Affiliations

¹ Candidate in Water Resources Engineering, Department of Hydrology and Water Resources, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
² Department of Hydrology and Water Resources, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
³ Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran.

Abstract

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.

Keywords: Hydropower operation; Karun-4 reservoir; Moth swarm algorithm; Optimization algorithms.