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

Akbarifard, Saeid; Sharifi, Mohammad Reza; Qaderi, Kourosh

doi:10.1016/j.dib.2019.105048

Cited by 19 publications

(16 citation statements)

References 3 publications

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“…The multilayer perceptron (MLP) architecture has been used successfully to model some difficult and diverse problems and nonlinear functions such as air quality forecasting. It is composed of a system of layered and interconnected neurons or nodes, namely, an input, one or more hidden layers, and output layers 15,28,29 . In the current study, logarithmic sigmoid and linear activation functions were examined to optimize the network.…”

Section: Multiple Linear Regression Model (Mlr)mentioning

confidence: 99%

“…In the current study, logarithmic sigmoid and linear activation functions were examined to optimize the network. The back-propagation (BP) training algorithm is found to be the most common and powerful nonlinear statistical technique in MLP networks 29,30 . All computations were developed in MATLAB R2016b software.…”

Section: Multiple Linear Regression Model (Mlr)mentioning

confidence: 99%

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Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

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Section: Multiple Linear Regression Model (Mlr)mentioning

confidence: 99%

Section: Multiple Linear Regression Model (Mlr)mentioning

confidence: 99%

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Shams

Jahani²,

Kalantary

et al. 2021

Sci Rep

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show abstract

“…A floating photovoltaic power plant could be affected by these variables and shading. [10] In this study, the output of six atmospheric circulation models was subscale with LARS-WG statistical model to investigate and analyze the effect of climate change on temperature and precipitation in the Karun 4 dam. The best model and scenario for the production of temperature and precipitation data for the period 2020 to 2040 were selected.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the benefits listed above, floating solar power plants have a strategic advantage in terms of Iran's location. Karun 4 Dam is the largest arch dam in the country and the world's fifth-largest dam and hydropower plant [10]. With its prior features and the utilization of new resources, this dam can be a pioneer in establishing the balance of energy management and a viable model for transitioning to renewable energy in the economic and executive management systems, as well as in energy policy [11].…”

mentioning

confidence: 99%

Floating Solar Power Plants: A Way to Improve Environmental and Operational Flexibility

Shayan

Hojati²

2021

IJEE

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The Photovoltaic modules installed on the surface of the water are naturally cooled, reducing the loss of thermal power generation. Floating photovoltaic systems (FPVS) combine existing photovoltaic systems with a floating structure to generate clean energy. To meet the growing electricity demand, FPV systems will be integrated alongside existing dams to enhance existing power sources. The results indicate that the investment toward installing FPV systems over the dams' reservoirs leads to a significant improvement in the overall system reliability minimizes load curtailment, and could potentially add more flexibility to the operator to dispatch power generated by hydropower plants during peak demands. The execution of the Karun-4 FPV power plant with an annual production of 16758969 kWh of energy has reduced the water evaporation of the dam's reservoir water and after eight years and four months, the investment cost was returned and its nominal performance is 81.7 percent. Adding a floating solar power plant with 10% of the lake reservoir cover of six dams saves 70.7 million cubic meters of water per year. This amount of fresh water is enough to meet the annual needs of one million people.

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“…One of the strongest single-objective algorithms within the family of evolutionary algorithms is the moth swarm algorithm (MSA). The MSA has been proven to be superior to over 80 other evolutionary algorithms [25][26][27][28][29][30][31][32][33] . Due to the novelty of the MSA, there is still no study in the literature to design a multi-objective version of this algorithm.…”

Section: A New Optimization Algorithm To Solve Multi-objective Problemsmentioning

confidence: 99%

A new optimization algorithm to solve multi-objective problems

Sharifi

Akbarifard

Qaderi

et al. 2021

Sci Rep

Self Cite

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Simultaneous optimization of several competing objectives requires increasing the capability of optimization algorithms. This paper proposes the multi-objective moth swarm algorithm, for the first time, to solve various multi-objective problems. In the proposed algorithm, a new definition for pathfinder moths and moonlight was proposed to enhance the synchronization capability as well as to maintain a good spread of non-dominated solutions. In addition, the crowding-distance mechanism was employed to select the most efficient solutions within the population. This mechanism indicates the distribution of non-dominated solutions around a particular non-dominated solution. Accordingly, a set of non-dominated solutions obtained by the proposed multi-objective algorithm is kept in an archive to be used later for improving its exploratory capability. The capability of the proposed MOMSA was investigated by a set of multi-objective benchmark problems having 7 to 30 dimensions. The results were compared with three well-known meta-heuristics of multi-objective evolutionary algorithm based on decomposition (MOEA/D), Pareto envelope-based selection algorithm II (PESA-II), and multi-objective ant lion optimizer (MOALO). Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were employed for comparison purposes. The qualitative and quantitative results indicated the superior performance and the higher capability of the proposed MOMSA algorithm over the other algorithms. The MOMSA algorithm with the average values of CPU time = 2771 s, GD = 0.138, S = 0.063, Δ = 1.053, and MS = 0.878 proved to be a robust and reliable model for multi-objective optimization.

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

Cited by 19 publications

References 3 publications

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Floating Solar Power Plants: A Way to Improve Environmental and Operational Flexibility

A new optimization algorithm to solve multi-objective problems

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