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JCR Impact Factor: 0.699
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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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  3/2016 - 10

Spatiotemporal Data Mining for Distribution Load Expansion

ARANGO, H. G. See more information about ARANGO, H. G. on SCOPUS See more information about ARANGO, H. G. on IEEExplore See more information about ARANGO, H. G. on Web of Science, LAMBERT-TORRES, G. See more information about  LAMBERT-TORRES, G. on SCOPUS See more information about  LAMBERT-TORRES, G. on SCOPUS See more information about LAMBERT-TORRES, G. on Web of Science, de MORAES, C. H. V. See more information about  de MORAES, C. H. V. on SCOPUS See more information about  de MORAES, C. H. V. on SCOPUS See more information about de MORAES, C. H. V. on Web of Science, BORGES DA SILVA, L. E. See more information about BORGES DA SILVA, L. E. on SCOPUS See more information about BORGES DA SILVA, L. E. on SCOPUS See more information about BORGES DA SILVA, L. E. on Web of Science
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Author keywords
data mining, intelligent systems, load modeling, power distribution, urban areas

References keywords
urban(9), power(9), economic(7), torres(6), systems(6), silva(6), economics(6), data(6), spatial(5), mining(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 65 - 72
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03010
Web of Science Accession Number: 000384750000010
SCOPUS ID: 84991087193

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The load spatial forecasting is fundamental for the electric energy distribution systems planning. Several methods using different conceptions have been proposed to determine the future configuration of the electric markets. This paper proposes a dynamic model of load expansion, based on concepts of local analysis using ideas and applications from urban poles theory. Thus, the load expansion is simulated in a dynamic way, maintaining a continuous change in the conditions for localization of a new load unit. An algorithm generating a snapshot that represents the distribution system configuration at that instant determines the geometry of the market in a given instant. The proposed dynamic model, based on the urban poles theory, has the capacity for summing up the information from economic variables sets, expressed in terms of interchange flow laws, which are modeled by distance and transportation functions. This supplies the model with the capacity for being used even though the number of available explanatory variables is reduced.

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

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[10] H. S. Moghadama, M. Helbich, "Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains - cellular automata urban growth model," Applied Geography, vol.40, pp.240-249, 2013.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 112]

[11] M. P. Coutinho, G. Lambert-Torres, L. E. Borges da Silva, H. Lazarek, "A Rough Set Classification Algorithm for Detecting Attacks on Electric Power Systems and Other Critical Structures," The International Journal of Forensic Computer Science, vol.3, no.1, pp.25-32, 2008.

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[14] R. B. Gonzatti, S. C. Ferreira, C. H. da Silva, L. E. Borges da Silva, R. R. Pereira, G. Lambert-Torres, "Smart Impedance: A New Way to Look at Hybrid Filters," IEEE Transactions on Smart Grid, vol.7, no.2, pp.837-846, 2016.
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References Weight

Web of Science® Citations for all references: 1,154 TCR
SCOPUS® Citations for all references: 1,704 TCR

Web of Science® Average Citations per reference: 41 ACR
SCOPUS® Average Citations per reference: 61 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 2019-01-18 00:34 in 160 seconds.

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