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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
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Next issue: Feb 2019
Avg review time: 83 days


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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  1/2008 - 3

The Role of Weather Indicators in Energy Consumption

NITU, C. See more information about NITU, C. on SCOPUS See more information about NITU, C. on IEEExplore See more information about NITU, C. on Web of Science, DOBRESCU, A. S. See more information about DOBRESCU, A. S. on SCOPUS See more information about DOBRESCU, A. S. on SCOPUS See more information about DOBRESCU, A. S. on Web of Science
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 (328 KB) | Citation | Downloads: 699 | Views: 3,111

Author keywords
energy consumption, polynomial regression, indoor comfort, prediction

References keywords
No relevant keywords could be extracted from the references.

About this article
Date of Publication: 2008-04-02
Volume 8, Issue 1, Year 2008, On page(s): 17 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.01003
Web of Science Accession Number: 000259903500003
SCOPUS ID: 77955594048

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This paper presents a method for predicting the consumption of energy. The prediction is realized for the energy used for heating, where the thermal comfort has an important role. The equations that define the thermal comfort in function of the weather indicators are also mandatory for the research.

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

[1] B. L. Capehart, L. C. Capehart, "Web based energy information and control system: Case Studies and Applications", CRC Press, 2005.

[2] Intelligent buildings - learning modules [Online] Available: Temporary on-line reference link removed - see the PDF document

[3] Memarzadeh, F., A. Manning, "Thermal Comfort, Uniformity, and Ventilation Effectiveness in Patient Rooms: Performance Assessment Using Ventilation Indices, In ASHRAE Transaction", 2000.

[4] C. Nitu, V. F. Krapivin, A. Bruno, "Modelarea proceselor in ecologie", Ed. Printech, Bucuresti, 2000.

[5] K. F. Geoffrey, K. W. Kelvin, "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks", 2007.
[CrossRef] [Web of Science Times Cited 227] [SCOPUS Times Cited 297]

[6] W. Popinski, "Consistency of trigonometric and polynomial regression estimation", Warsaw, Applied Mathematics, 1998.

References Weight

Web of Science® Citations for all references: 227 TCR
SCOPUS® Citations for all references: 297 TCR

Web of Science® Average Citations per reference: 38 ACR
SCOPUS® Average Citations per reference: 50 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 2019-02-15 20:46 in 10 seconds.

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

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