|1/2014 - 19|
Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning MethodsDURSUN, B. , AYDIN, F. , ZONTUL, M. , SENER, S.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (777 KB) | Citation | Downloads: 378 | Views: 1,534|
electricity load forecasting, machine learning, multilayer perceptron, rule based learning, time series prediction
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
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|
| Y. Rui, A. A. El-Keib, "A review of ANN-based short-term load forecasting models", In Proceedings of the 27th Southeastern Symposium on System Theory. IEEE Computer Society, Washington, DC, USA, 1995, pp. 78-82.
 A. L. Samuel, "Some studies in machine learning using the game of checkers", IBM Journal of Research and Development, vol. 3, no. 3, pp. 210 229, 1959.
 R. Kohavi, F. Provost, "Glossary of Terms", Machine Learning, vol. 30, pp. 271-274, 1998.
 R. O. Duda, P. E. Hart, D. G. Stork, "Pattern classification - 2nd ed.", New York, Wiley-Interscience, pp. 680, 2000.
 M. Negnevitsky, P. Mandal, A. K. Srivastava, "Machine Learning Applications for Load Price and Wind Power Prediction in Power Systems", 15th International Conference on Intelligent System Application to Power Systems, Curitiba, Brazil, 2009, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 19]
 S. Fan, L. Chen, W. Lee, "Machine learning based switching model for electricity load forecasting", Energy Conversion and Management, vol. 49, no. 6, pp. 1331-1344, 2008.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 30]
 Y. Guo , D. Niu, Y. Chen, "Support Vector Machine Model in Electricity Load Forecasting", Machine Learning and Cybernetics, 2006 International Conference, 2006, pp. 2892-2896.
 R. A. Swief, Y. G. Hegazy, T. S. Abdel-Salam, M.A Bader "Load Price Forecasting Model Employing Machine Learning Techniques", The Online Journal on Power and Energy Engineering, vol. 1, no. 2, pp. 36-39, 2010.
 S. Russell, P. Norvig, "Artificial Intelligence: A Modern Approach", Prentice Hall, 2002.
 I. H. Witten, E. Frank, M. A. Hall, "Data Mining: Practical machine learning tools and techniques", 3rd Edition, Morgan Kaufmann, 2011.
 M. Riedmiller, "Advanced Supervised Learning in Multi-layer Perceptrons From Backpropagation to Adaptive Learning Algorithms", Computer Standards & Interfaces, vol. 16, no. 3, pp. 265 278, 1994.
 H. Zhang, "The Optimality of Naive Bayes", Proceedings of the 17th International FLAIRS conference (FLAIRS2004), 2004, pp. 562 567.
 G. H. John, P. Langley, "Estimating Continuous Distributions in Bayesian Classifiers", Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 1995, pp. 338 345.
 E. Frank, M. Hall, B. Pfahringer, "Locally Weighted Naive Bayes", In: 19th Conference in Uncertainty in Artificial Intelligence, 2003, pp. 249 256.
 C. G. Atkeson, A. W. Moore, S. Schaal, "Locally Weighted Learning", Artificial Intelligence, vol. 11, pp. 11 73, 1997.
 U. M. Fayyad, K. B. Irani, "Multi interval discretization of continuous valued attributes for classification learning", In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, 1993, pp. 1022 1027.
 R. J. Quinlan, "Learning with Continuous Classes", In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 1992, pp. 343 348.
 Y. Wang, I. H. Witten, "Induction of model trees for predicting continuous classes", In: Poster papers of the 9th European Conference on Machine Learning, 1997.
 J. G. Zhang, H. W. Deng, "Gene selection for classification of microarray data based on the Bayes error", BMC Bioinformatics, vol. 8, pp. 370, 2007.
[CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 56]
 I. H. Witten, E. Frank, "Datamining: practical machine learning tools and techniques 2nd ed.", the United States of America, Morgan Kaufmann series in data management systems, 2005.
 J. Cohen, "Statistical power analysis for the behavioral sciences (2nd ed.)", Lawrence Erlbaum Associates, pp. 567, 1988.
 E. L. Lehmann, G. Casella, "Theory of Point Estimation (2nd ed.)", New York: Springer, 1998.
 P. Domingos, "A Unifed Bias-Variance Decomposition and its Applications", In Proc. 17th International Conf. on Machine Learning, 2000, pp. 231 238.
 S. Geman, E. Bienenstock, R. Doursat, "Neural networks and the bias/variance dilemma", Neural Computation, vol. 4, no. 1, pp. 1 58, 1992.
 E. Alpaydin, "Introduction to Machine Learning", the United States of America, The MIT Press, 2004.
 R. Kohavi, "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection", Proc. of the 14th Int. Joint Conf. on A.I., Canada, 1995.
 A. Lendasse, V. Wertz, M. Verleysen, "Model Selection with Cross-Validations and Bootstraps Application to Time Series Prediction with RBFN Models", Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, O. Kaynak, E. Alpaydin, E. Oja, L. Xu eds, Springer-Verlag, Lecture Notes in Computer Science Vol. 2714, pp. 573-580, 2003.
 T. M. Mitchell, "Machine Learning", McGraw-Hill Science/Engineering/Math, pp. 432, 1997.
 N. J. Nilsson, "Introduction to Machine Learning: An Early Draft of a Proposed Textbook", Robotics Laboratory, Department of Computer Science, Stanford University, 1996.
 R. R. Bouckaert, E. Frank, M. Hall, R. Kirkby, P. Reutemann, A. Seewald, D. Seuse, "WEKA Manual for Version 3-6-0", The University of Waikato, 2008.
 D. H. Wolpert, W. G. Macready, "No Free Lunch Theorems for Optimization", IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, 1997.
[CrossRef] [SCOPUS Times Cited 2941]
 D. H. Wolpert, W. G. Macready, "Coevolutionary free lunches", IEEE Transactions on Evolutionary Computation, vol. 9, no. 6, pp. 721-735, 2005.
[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 73]
 C. Luque, D. Quintana, P. Isasi, "Predicting IPO underpricing with genetic algorithms", International Journal of Artificial Intelligence, vol. 8, no. S12, pp. 133-146, 2012.
 G. Prakash, M. Kulkarni, U. S. Acharya, M. N. Kalyanpur, "Classification of FSO Channel Models Using Radial Basis Function Neural Networks and Their BER Performance with Luby Transform Codes", International Journal of Artificial Intelligence, vol. 9, no. A12, 2012.
 I. Martiius, K. Sidlauskas, R. Damasevicius, "Real-Time Training of Voted Perceptron for Classification of EEG Data", International Journal of Artificial Intelligence, vol. 10, no. S13, 2013.
 A. Ismail, D.-S. Jeng, L. L. Zhang, J.-S. Zhang, "Predictions of bridge scour: Application of a feed-forward neural network with an adaptive activation function", Engineering Applications of Artificial Intelligence, vol. 26, no. 5-6, pp. 1540-1549, 2013.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 15]
Web of Science® Citations for all references: 127 TCR
SCOPUS® Citations for all references: 3,134 TCR
Web of Science® Average Citations per reference: 3 ACR
SCOPUS® Average Citations per reference: 85 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 2017-04-27 01:10 in 50 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.