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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: May 2017
Next issue: Aug 2017
Avg review time: 74 days


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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
Issue 3/2016

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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  1/2014 - 8

Advanced PV Plant Planning based on Measured Energy Production Results - Approach and Measured Data Processing

PETROVIC, I. See more information about PETROVIC, I. on SCOPUS See more information about PETROVIC, I. on IEEExplore See more information about PETROVIC, I. on Web of Science, SIMIC, Z. See more information about  SIMIC, Z. on SCOPUS See more information about  SIMIC, Z. on SCOPUS See more information about SIMIC, Z. on Web of Science, VRAZIC, M. See more information about VRAZIC, M. on SCOPUS See more information about VRAZIC, M. on SCOPUS See more information about VRAZIC, M. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (905 KB) | Citation | Downloads: 415 | Views: 1,713

Author keywords
energy capture, power control, power system analysis computing, power system measurements, photovoltaic cells

References keywords
energy(18), photovoltaic(17), renewable(7), solar(5), systems(4), simic(4), prediction(4), power(4), monitoring(4), assessment(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-02-28
Volume 14, Issue 1, Year 2014, On page(s): 49 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01008
Web of Science Accession Number: 000332062300008
SCOPUS ID: 84894611088

Abstract
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The objective of this research was to develop a model for a prototype measurement station built for recording data relevant for the production of electrical energy by PV modules in order to improve the modelling. The measured data for specific time period (annually), or periods (monthly or otherwise), was processed after measurement results are available, and allow better and more accurate planning of the PV plant. Therefore, data processing time is not significant since it is not used in real-time. Measurement station was designed in a way which allows for the total energy production to be quantified for various modes of operation (fixed, single-axis or dual-axis tracking). In this article energy production in weekly time periods is analyzed since data for short time periods is available and the main goal is to find the most accurate method in PV module characterization. Once the data are obtained, it will be possible to propose improvements of conventional analytical models when measured and modelled results are compared. These improvements will be specific for the location on which prototype measurement station is installed. The proposed improvements will allow upgrades in model estimations without the need for additional measurements by a prototype measurement station.


References | Cited By  «-- Click to see who has cited this paper

[1] Renewable Energy Index: Average Cost of Solar Panels and Installation [Online] Available: Temporary on-line reference link removed - see the PDF document

[2] T. M. Razykov, C. S. Ferekides, D. Morel, E. Stefanakos, H. S. Ullal, H. M. Upadhyaya: Solar photovoltaic electricity: current status and future prospects, Solar Energy 85, 2011, pages 1580-1608
[CrossRef] [Web of Science Times Cited 360]


[3] Joint Research Center (JRC) of the Commission of the European Communities: Guidelines for the Assessment of Photovoltaic Plants, Document A, Version 4.3, Photovoltaic System Monitoring, March 1997.

[4] Joint Research Center (JRC) of the Commission of the European Communities: Guidelines for the Assessment of Photovoltaic Plants, Document B, Version 4.3, Photovoltaic System Monitoring, March 1997.

[5] M. Raugei, P. Frankl.: Life cycle impacts and costs of photovoltaic systems: current state of the art and future outlooks, Journal of Energy 34, 2009, pages 392-399
[CrossRef] [Web of Science Times Cited 86] [SCOPUS Times Cited 105]


[6] R. A. Shayani, M. A. Gonçalves de Oliveira: A New Index for Absolute Comparison of Standalone Photovoltaic Systems Installed at Different Locations, IEEE transactions on sustainable energy, Vol. 2, No. 4, October 2011, pages 495-500

[7] R. Messenger, J. Ventre: Photovoltaic Systems Engineering, 3rd ed., CRC Press, Taylor & Francis Group, Boka Ration, Fla., USA, 2010

[8] Photovoltaic System Performance Monitoring—Guidelines for Measurement, Data Exchange and Analysis, International Standard IEC 61724, International Electrotechnical Commission (IEC), 1998, First ed., Geneve

[9] A. Coelho, R. Castro: Sun Tracking PV Power Plants: Experimental Validation of Irradiance and Power Output Prediction Models, International journal of Renewable energy research, Vol.2, No.1, 2012

[10] E. Kymakis, S. Kalykakis, T. M. Papazoglou: A photovltaic park's performance on the island of Crete, Energija 57 (2008), Nr. 3, pages 300-311

[11] J. I. Rosell, M. Ibanez, "Modelling power output in photovoltaic modules for outdoor operating conditions," Energy Conversion and Management 47 (2006), pages 2424-2430
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 73]


[12] C. Carrero, J. Rodriguez, D. Ramirez, C. Platero: Simple estimation of PV modules loss resistances for low error modelling, Elsevier, Renewable Energy 35 (2010), pages 1103-1108
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 69]


[13] W. Zhou, H. Yang, Z. Fang: A novel model for photovoltaic arrayperformance prediction, Applied Energy 84, 2007, pages 1187-1198
[CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 143]


[14] R. Chenni, M. Makhlouf, T. Kerbache, A. Bouzid: A detailed modeling method for photovoltaic cells, Energy 32 (2007), pages 1724-1730
[CrossRef] [Web of Science Times Cited 184] [SCOPUS Times Cited 236]


[15] M. Katsanevakis, "Modelling the Photovoltaic Module," IEEE International Symposium on Industrial Electronics, 2011, pages 1414-1419

[16] A. Senpinar, M. Cebeci, "Evaluation of power output for fixed and two-axis tracking Pvarrays," Journal of Energy 92, 2012, pages 677-685

[17] T. Tomisa, Z. Simic, D. Dedeic: Automated photovoltaic panel positioning device for solar radiation monitoring, MIPRO Computers in technical systems, 2011, pages 28-33

[18] R. Pasicko, C. Brankovic, Z. Simic: Assessment of Climate Change Impacts on Energy Generation from Renewable Sources in Croatia, Generation from RES Croatia, Journal of Renewable Energy 46, October 2012, pages 224-231
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 25]


[19] F. J. Toledo, J. M. Blanes, A. Garrigos, J. A. Martínez: Analytical resolution of the electrical four-parameters model of a photovoltaic module using small perturbation around the operating point, Journal of Renewable energy 43, 2012, pages 83-89

[20] P. Kulusic, J. Vuletin, I. Zulim: Solar cells, Skolska knjiga, Zagreb, 1994

[21] J. Kaldellis, D. Zafirakis: Experimental investigation of the optimum photovoltaic panels’ tilt angle during the summer period, Energy 38, 2012, pages 305-314
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[22] T. Tsoutsos, I. Mavrogiannis, N. Karapanagiotis, St. Tselepis, D. Agoris, "An analysis of the Greek photovoltaic market," Renewable & Sustainable Energy Reviews (8), 2004, pages 49-72

[23] A. M. Papadopoulos, M. M. Karteris: An assessment of the Greek incentives scheme for photovoltaics, Energy Policy, 2009, Vol 37, Issue 5, pages 1945-1952
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[24] I. Petrovic, Z. Simic, M. Vrazic: Comparison of PV plant energy generation prediction tools with measured data, Journal of Przeglad Elektrotechniczny, june 2013., pages 121-124

[25] I. Petrovic, Z. Simic, M. Vrazic: Advancements in PV plant energy production prediction with model improvement based on measured data, Journal of International Review of Electrical Engineering (I.R.E.E.), Vol 8, Issue 2, april 2013, pages 832-838



References Weight

Web of Science® Citations for all references: 922 TCR
SCOPUS® Citations for all references: 720 TCR

Web of Science® Average Citations per reference: 35 ACR
SCOPUS® Average Citations per reference: 28 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-08-14 10:06 in 67 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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