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

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


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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.

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.

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  1/2009 - 11

Clustering Techniques in Load Profile Analysis for Distribution Stations

Click to see author's profile in 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 (501 KB) | Citation | Downloads: 996 | Views: 5,803

Author keywords
load profile, clustering techniques, data flow analysis, power consumption, distribution station

References keywords
power(5), load(5), clustering(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2009-02-03
Volume 9, Issue 1, Year 2009, On page(s): 63 - 66
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.01011
Web of Science Accession Number: 000264815300011
SCOPUS ID: 67749137135

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The demand characteristic is the most important one in analyzing customer information. In a distribution network, there is in any moment certain degree of uncertainty about busses loads, and consequently, about load level of network, busses voltage level, and power losses. Therefore, it is very important to estimate first of all the load profiles of buses, using available data (measurements effectuated in distribution stations). The results obtained for various distribution stations demonstrate the effectiveness of the present method in overcoming the difficulties encountered in optimal planning and operation of distribution networks.

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

[1] Load profiles and their use in electricity settlement, Electricity Association, Publisher UKERC, 1997

[2] "Handbook of Applied Multivariate Statistics and Mathematical Modelling", Edited by: Howard E.A. Tinsley and Steven D. Brown ISBN: 978-0-12-691360-6 [PermaLink]

[3] Gh. Cartina, Gh. Grigoras., E.C. Bobric, "Clustering Techniques in Load Analyse", Proc. of the International Power Systems Conference, PSC'05, 2005, Timisoara, Romania, pp. 123-130

[4] R.F. Chang, C.N. Lu, "Load profiling and its applications in power market", Power Engineering Society General Meeting, 2003, IEEE Volume 2, 13-17 July 2003,
[CrossRef] [Web of Science Record]

[5] C. Nitu, A. S. Dobrescu, "The Role of Weather Indicators in Energy Consumption", Advances in Electrical and Computer Engineering, Suceava, Romania, ISSN 1582-7445, No 1/2008, volume 8, pp. 17-20
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]

[6] JMP Statistics and Graphics Guide: Version 3, SAS Institute Inc., Cary, NC, USA, 1999

[7] A.K. Jain, M.N. Murty, P.J. Flynn, Data Clustering: A Rewiew, ACM Computing Serveys, 31, 264-323, 1999
[CrossRef] [Web of Science Times Cited 5428] [SCOPUS Times Cited 7817]

[8] Clustering: An Introduction, Available:

[9] Gh. Cartina, Gh. Grigoras, E.C. Bobric, "Clustering Techniques in Fuzzy Modeling. Power Systems Applications", Casa de Editura VENUS, Iasi, 2005

[10] P.E. Sinioros, C. Filote, A. Graur, M.G. Ioannides, "A New Real Time Method of the Instantaneous Active and Reactive Power Calculus", Advances in Electrical and Computer Engineering, Suceava, Romania ISSN 1582-7445, No. 1/2001, volume 1 (8), pp. 5-10

[11] M. Gavrilas, V.C. Sfintes, M.N. Filimon, "Identifying typical load profiles using neural-fuzzy models", 16th IEEE/PES Transmission and Distribution Conf. and Exposition, 2001, Atlanta, pp. 421-426

[12] M. Gavrilas, Gh. Cartina, Gh. Grigoras, O. Ivanov, "Modelarea sarcinilor din retelele electrice", Editura PIM, Iasi, 2006

[13] D. Gerbec, S. Gasperic, and F. Gubina, "Comparison of Different Classification Methods for the Consumers' Load Profile Determination", 17th International Conference on Electricity Distribution, CIRED, Barcelona, vol. Session 6, 2003.

References Weight

Web of Science® Citations for all references: 5,430 TCR
SCOPUS® Citations for all references: 7,820 TCR

Web of Science® Average Citations per reference: 388 ACR
SCOPUS® Average Citations per reference: 559 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 2019-02-16 17:46 in 37 seconds.

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

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