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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: 644266260
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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  1/2014 - 2

A Combined Methodology of Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm for Short-term Energy Forecasting

KAMPOUROPOULOS, K. See more information about KAMPOUROPOULOS, K. on SCOPUS See more information about KAMPOUROPOULOS, K. on IEEExplore See more information about KAMPOUROPOULOS, K. on Web of Science, ANDRADE, F. See more information about  ANDRADE, F. on SCOPUS See more information about  ANDRADE, F. on SCOPUS See more information about ANDRADE, F. on Web of Science, GARCIA, A. See more information about  GARCIA, A. on SCOPUS See more information about  GARCIA, A. on SCOPUS See more information about GARCIA, A. on Web of Science, ROMERAL, L. See more information about ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on Web of Science
 
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Download PDF pdficon (682 KB) | Citation | Downloads: 666 | Views: 2,317

Author keywords
adaptive neuro-fuzzy inference system, energy forecast, genetic algorithm, intelligent energy management systems

References keywords
energy(13), systems(9), load(7), neural(6), fuzzy(6), applications(6), term(5), short(5), optimization(5), network(5)
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): 9 - 14
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.01002
Web of Science Accession Number: 000332062300002
SCOPUS ID: 84894611007

Abstract
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This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a short-term load forecasting for the different modeled consumption processes.


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

[1] A. Tisot, "Industrial energy management: Doing more with less," Pulp & Paper-Canada, vol. 105, pp. 21-23, Oct 2004.

[2] R. E. Precup, R. C. David, E. M. Petriu, M.B. Radac, S. Preitl, J. Fodor, "Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems," Knowledge-Based Systems, vol. 38, pp. 74-84, 2013.
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 64]


[3] P. J. Santos, A. G. Martins, and A. J. Pires, "Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems," International Journal of Electrical Power & Energy Systems, vol. 29, May 2007.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 42]


[4] Z. Xiao, S. J. Ye, B. Zhong, C. X. Sun, "BP neural network with rough set for short term load forecasting," Expert Systems with Applications, vol. 36, pp. 273-279, 2009.
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 119]


[5] S. Kouhi, F. Keynia, "A new cascade NN based method to short-term load forecast in deregulated electricity market," Energy Conversion and Management, vol. 71, pp. 76-83, 2013.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 18]


[6] A. Badri, Z. Ameli, A. M. Birjandi, "Application of artificial neural networks and fuzzy logic methods for short term load forecasting," 2nd International Conference on Advances in Energy Engineering, vol. 14, 2012.

[7] F. R. Fulginei, A. Laudani, A. Salvini, M. Parodi, "Automatic and parallel optimized learning for neural networks performing MIMO applications," Advances in Electrical and Computer Engineering, vol. 13, no. 1, pp. 3-12, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 20] [SCOPUS Times Cited 27]


[8] L. C. Ying, M. C. Pan, "Using adaptive network based fuzzy inference system to forecast regional electricity loads," Energy Conversion and Management, vol. 49, 2008.

[9] O. Brudaru, D. Popovici, C. Copaceanu, "Cellular genetic algorithm with communicating grids for assembly line balancing problems," Advances in Electrical and Computer Engineering, vol. 10, no. 2, pp. 87-93, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]


[10] M. Z. Ali, K. Alkhatib, Y. Tashtoush, "Cultural algorithms: Emerging social structures for the solution of complex optimization problems," International Journal of Artificial Intelligence, vol. 11, no. A13, pp. 20-42, 2013.

[11] D. Wieland, F. Wotawa, G. Wotawa, "From neural networks to qualitative models in environmental engineering," Computer-Aided Civil and Infrastructure Engineering, vol. 17, pp. 104-118, 2002.
[CrossRef] [Web of Science Times Cited 17]


[12] A. Mellit, S. Saglam, S. A. Kalogirou, "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, vol. 60, pp. 71-78, 2013.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 29]


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[14] A. Khotanzad, E. Zhou, H. Elragal, "A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment", IEEE Power Engineering Review, vol. 22, pp. 55, 2002.
[CrossRef]


[15] J. J. Cardenas, A. Garcia, J. L. Romeral, K. Kampouropoulos, "Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory," The IEEE 2011 Conference on Emerging Technologies and Factory Automation, 2011.

[16] T. Takagi, M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," Ieee Transactions on Systems Man and Cybernetics, vol. 15, 1985.

[17] D. X. Niu, Y. L. Wang, D. Wu, "Power load forecasting using support vector machine and ant colony optimization," Expert Systems with Applications, vol. 37, pp. 2531-2539, 2010.
[CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 117]


[18] K. Kampouropoulos, F. Andrade, J.J. Cárdenas, J.L. Romerar, "A Methodology for Energy Prediction and Optimization of a System based on the Energy Hub Concept using Particle Swarms," The Annual Seminar on Automation, Industrial Electronics and Instrumentation, 2012.

[19] R. Poli, W. B. Langdon, N. F. McPhee and J. R. Koza, "Genetic Programming An Introductory Tutorial and a Survey of Techniques and Applications," CES-475, ISSN: 1744-8050, 2007.

[20] G. Renner, A. Ekart, "Genetic algorithms in computer aided design," Computer-Aided Design, vol. 35, 2003.

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[22] E. C. Brown, R. T. Sumichrast, "Evaluating performance advantages of grouping genetic algorithms," Engineering Applications of Artificial Intelligence, vol. 18, 2005.

[23] J. Sheppard, A. Tisot, "Industrial energy management: Doing more with less", Industrial Energy Technology Conference, 2006.

[24] P. A. Gonzalez, J. M. Zamarreno, "Prediction of hourly energy consumption in buildings based on a feedback artificial neural network", Energy and Buildings, vol. 37, pp. 595-601, 2005.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 129]




References Weight

Web of Science® Citations for all references: 399 TCR
SCOPUS® Citations for all references: 551 TCR

Web of Science® Average Citations per reference: 16 ACR
SCOPUS® Average Citations per reference: 22 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 2016-12-02 06:08 in 60 seconds.




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


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