The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey

Ceylan, İlhan; Gedik, Engin; Erkaymaz, Okan; Gürel, Ali Etem

doi:10.1016/j.enbuild.2014.08.003

Cited by 25 publications

(6 citation statements)

References 22 publications

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“…Ceylan et al have formulated an ANN model to forecast the temperature, power output and efficiency of photovoltaic system using solar insolation and outlet air temperature as the input conditions of Karabuk, Turkey. Prediction accuracy of the model could be enhanced by incorporating the effect of ambient air velocity and relative humidity 104 . Mekki et al have developed an ANN model to detect the faults in photovoltaic module operated under various partially shaded conditions located at Renewable Energy Laboratory in Jijel University, Algeria.…”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Prediction of the efficiency that can be obtained from environmental factors in areas where photovoltaic power systems will be installed plays a critical role in avoiding erroneous installation and prevention of unnecessary investments. Artificial neural network applications-such as estimation and modelling of daily solar radiation data by using sunshine duration and temperature data in the photovoltaic power system [20], radial basis function for the estimation of output characteristic of the photovoltaic module by using solar radiation and temperature [21], performance estimation by using solar radiation in an on-grid-connected photovoltaic power system [22], estimation of power output by using solar radiation and temperature in a 1 MW solar power plant [23], estimation of the power for the photovoltaic system a day ahead by using the solar radiation parameter [24], use of solar radiation and temperature parameters as input for performance estimation of a 20 kWp gridconnected photovoltaic plant [25], modelling by using temperature and solar radiation and neighbouring photovoltaic system data in power estimation [26] and estimation of module temperature and efficiency of the photovoltaic modules for all regions in Turkey by using ambient temperature and solar radiation [27], and estimating the efficiency of a photovoltaic cell of 4.2 V-100 mA depending on wind velocity, temperature, humidity, and horizontal angle of the cell [28]-draw attention.…”

Section: Wind Velocity Effect On Pv Cellsmentioning

confidence: 99%

Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa

İçel

Mamiş

Buğutekin

et al. 2019

International Journal of Photoenergy

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The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.

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“…However, these conditions are not always obvious, occurring seldom outside, because they are mainly carried out under conditions of the laboratory by using a solar simulator matirials. Consequently, to carry out a characterization appropriate to the behavior of electric modules regular minutes (obtaining curves I-V and P-V), recently, several authors [5][6] are used the artificial intelligence technics such as the fuzzy logic [5][6][7] and the artificial neuron networks (ANN) [2,[6][7][8][9][10][11][12][13] to modeling OPV cells. This approach is logical if one were to consider the dependence of the solar cell to any variations conditions of the environment [8].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Ghaitaoui¹,

Benatiallah²,

Khachab³

et al. 2019

EJEE

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In this paper, the characterization and modeling results of the electrical parameters of the tendem organic photovoltaic cells (infinityPV) are presented. The electrical performances of this organic cell's module (I-V and P-V) are characterized and analyzed in the weather condition of southwest Algeria (Adrar site). A program in MATLAB language is developed locally for modeling the organic cell, using artificial intelligence. The multilayer feed forward perception type ANN is used for extraction of the organic photovoltaic model performances, and the back-propagation algorithm is used for the program training. The ANN obtained results by the simulation showed a good conformity with the results obtained experimentally in the saharan climate (MAPE:0.68%, MBE:4.5018e-09 A and RMSE:1.8062e-04 A).

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The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey

Cited by 25 publications

References 22 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

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