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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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  1/2014 - 19

 HIGHLY CITED PAPER 

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
 
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Download PDF pdficon (777 KB) | Citation | Downloads: 567 | Views: 2,879

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), hall(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.


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

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References Weight

Web of Science® Citations for all references: 221 TCR
SCOPUS® Citations for all references: 6,603 TCR

Web of Science® Average Citations per reference: 6 ACR
SCOPUS® Average Citations per reference: 178 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 2021-10-14 10:23 in 51 seconds.




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