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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Nov 2017
Next issue: Feb 2018
Avg review time: 105 days


PUBLISHER

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.
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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
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

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 - 19

Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods

DURSUN, B. See more information about DURSUN, B. on SCOPUS See more information about DURSUN, B. on IEEExplore See more information about DURSUN, B. on Web of Science, AYDIN, F. See more information about  AYDIN, F. on SCOPUS See more information about  AYDIN, F. on SCOPUS See more information about AYDIN, F. on Web of Science, ZONTUL, M. See more information about  ZONTUL, M. on SCOPUS See more information about  ZONTUL, M. on SCOPUS See more information about ZONTUL, M. on Web of Science, SENER, S. See more information about SENER, S. on SCOPUS See more information about SENER, S. on SCOPUS See more information about SENER, S. 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 (777 KB) | Citation | Downloads: 401 | Views: 1,709

Author keywords
electricity load forecasting, machine learning, multilayer perceptron, rule based learning, time series prediction

References keywords
learning(18), machine(14), artificial(11), intelligence(10), neural(6), model(6), power(5), load(5), classification(5), zhang(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): 121 - 132
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01019
Web of Science Accession Number: 000332062300019
SCOPUS ID: 84894610981

Abstract
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In this study, the electricity load demand, between 2012 and 2021, has been estimated using the load demand of the electricity generated from hydroelectric power plants in Turkey between 1970 and 2011. Among machine learning algorithms, Multilayer Perceptron, Locally Weighted Learning, Additive Regression, M5Rules and ZeroR classifiers are used to estimate the electricity load demand. Among them, M5Rules and Multilayer Perceptron classifiers are observed to have better performance than the others. ZeroR classifier is a kind of majority classifier used to compare the performances of other classifiers. Locally Weighted Learning and Additive Regression classifiers are Meta classifiers. In the training period conducted by Locally Weighted Learning and Additive Regression classifiers, when Multilayer Perceptron and M5Rules classifiers are chosen respectively, it is possible to obtain models with the highest performance. As a result of the experiments performed using M5Rules and Multilayer Perceptron classifiers, correlation coefficient values of 0.948 and 0.9933 are obtained respectively. And, Mean Absolute Error and Root Mean Squared Error value of Multilayer Perceptron classifier are closer to zero than that of M5Rules classifier. Therefore, it can be said the model performed by Multilayer Perceptron classifier has the best performance compared to the models of other classifiers.


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Cited-By CrossRef

[1] A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting, Razak, Intan Azmira Wan Abdul, Abidin, Izham Zainal, Yap, Keem Siah, Abidin, Aidil Azwin Zainul, Rahman, Titik Khawa Abdul, Nasir, Mohd Naim Mohd, 2016 IEEE International Conference on Power and Energy (PECon), ISBN 978-1-5090-2547-3, 2016.
Digital Object Identifier: 10.1109/PECON.2016.7951593
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Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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