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JCR Impact Factor: 0.699
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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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LATEST NEWS

2018-Jun-27
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.

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-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.

Read More »


    
 

  4/2010 - 17

An advanced strategy for wind speed forecasting using expert 2-D FIR filters

MOGHADDAM, A. A. See more information about MOGHADDAM, A. A. on SCOPUS See more information about MOGHADDAM, A. A. on IEEExplore See more information about MOGHADDAM, A. A. on Web of Science, SEIFI, A. R. See more information about SEIFI, A. R. on SCOPUS See more information about SEIFI, A. R. on SCOPUS See more information about SEIFI, A. R. 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 (2,239 KB) | Citation | Downloads: 1,259 | Views: 2,882

Author keywords
energy forecasting, FIR filters, image processing, 2-D linear filtering, wind speed

References keywords
wind(17), energy(15), power(11), speed(6), prediction(6), fuzzy(5), forecasting(5), term(4), neural(4), application(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-11-30
Volume 10, Issue 4, Year 2010, On page(s): 103 - 110
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.04017
Web of Science Accession Number: 000284782700017
SCOPUS ID: 78649690688

Abstract
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Renewable energies such as wind and solar have become the most attractive means of electricity generation nowadays. Social and environmental benefits as well as economical issues result in further utilization of such these energy resources. In this regard, wind energy plays an important roll in operation of small-scale power systems like Micro Grid. On the other hand, wind stochastic nature in different time and place horizons, makes accurate forecasting of its behavior an inevitable task for market planners and energy management systems. In this paper an advanced strategy for wind speed estimation has been purposed and its superior performance is compared to that of conventional methods. The model is based on linear predictive filtering and image processing principles using 2-D FIR filters. To show the efficiency of purposed predictive model different FIR filters are designed and tested through similar data. Wind speed data have been collected during the period January 1, 2009 to December 31, 2009 from Casella automatic weather station at Plymouth. It is observed that 2-D FIR filters act more accurately in comparison with 1-D conventional representations; however, their prediction ability varies considerably through different filter sizing.


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

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[2] Fadare D. A., "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria", Appl Energy 2010; 87(3): 934-42.
[CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 117]


[3] Kaldellis J. K, Kavadias K. A., Filios A. E., "A new computational algorithm for the calculation of maximum wind energy penetration in autonomous electrical generation systems", Appl Energy 2009; 86(7-8):1011-23.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 56]


[4] Sfetsos A., "A comparison of various forecasting techniques applied to mean hourly wind speed time series", Renew Energy 2000; 21(1):23-35.
[CrossRef] [Web of Science Times Cited 186] [SCOPUS Times Cited 255]


[5] Luickx P. J., Delarue E. D., D'Heseleer W. D., "Considerations on the backup of wind power: operational backup", Appl Energy 2008;85(9):787-99.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 41]


[6] Costa A., Crespo A., Navarro J., Lizcano G., Madsen H, Feitosa E, "A review on the young history of the wind power short-term prediction", Renew Sustain Energy Rev 2008; 12(6):1725-44.
[CrossRef] [Web of Science Times Cited 292] [SCOPUS Times Cited 394]


[7] Lei M., Shiyan L., Chuanwen J., Hongling L., Yan Z, "A review on the forecasting of wind speed and generated power", Renew Sustain Energy Rev 2009; 13(4): 915-20.
[CrossRef] [Web of Science Times Cited 325] [SCOPUS Times Cited 496]


[8] Watson S. J., Landberg L., Halliday J. A., "Application of wind speed forecasting to the integration of wind energy into a large scale power system", IEE Proc Gen Transm Distrib 1994; 141(4):357-62.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 67]


[9] G. Giebel, L. Landberg, G. Kariniotakis, and R. Brownsword, "State-of-the-art on methods and software tools for short-term prediction of wind energy production", in Proc. EWEC, Madrid, Spain, 2003.

[10] L. Landberg, G. Giebel, H. A. Nielsen, T. Nielsen, and H. Madsen, "Short-term prediction—An overview", Wind Energy (Special Review Issue on Advances in Wind Energy), vol. 6, no. 3, pp. 273-280, Jun. 2003.
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 107]


[11] T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis, and P. S. Dokopoulos, "Long-term wind speed and power forecasting using local recurrent neural network models", IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 273-284, Mar. 2006.
[CrossRef] [Web of Science Times Cited 202] [SCOPUS Times Cited 289]


[12] Lapedes A., Farber R., "Nonlinear signal processing using neural networks: prediction and system modeling", Technical report LA-UR-87-2662. Los Alamos, NM: Los Alamos National Laboratory; 1987.

[13] Kariniotakis G., Stavrakakis G. S., Nogaret E. F., "A fuzzy logic and neural network based wind power model", In: Proceeding the 1996 European wind energy conference. Goteborg (Sweden); 1996. p.596-599.

[14] Kim I, Lee SH, "A fuzzy time series prediction method based on consecutive values", In: Proceedings of the IEEE international fuzzy systems conference, vol. 2, Seoul, Korea; August 22-25 1999. p. 703-707.
[CrossRef]


[15] Damousis IG, Dokopoulos P, "A fuzzy expert system for the forecasting of wind speed and power generation in wind farms", In: 22nd IEEE Power Engineering Society international conference on power industry computer applications, 2001. PICA 2001. Innovative computing for power - electric energy meets the market; May 20-24 2001. p. 63-69.
[CrossRef] [Web of Science Times Cited 21]


[16] Hocaoglu, F. O., Gerek, O. N., Kurban, M. "A novel 2-D model approach for the prediction of hourly solar radiation", LNCS Springer 4507, 2007, 741-749.
[CrossRef]


[17] Intersil, "An introduction to digital filters", Application note, 1999, AN9603.2

[18] Scott C. Douglas, "Introduction to Adaptive Filters", Digital Signal Processing Handbook (1999) 7-12.

[19] Mark Nelson and Jean-Loup Gailly. "Speech Compression", The Data Compression Book (1995) 289-319.

[20] Rodgers, J. L. and Nicewander, W. A. (1988), "Thirteen ways to look at the correlation coefficient", The American Statistician 42: 59-66.
[CrossRef] [Web of Science Times Cited 1180]


[21] Gonzalez, R. C., Woods, R. E. "Digital Image Processing", second ed. Prentice-Hall, Englewood Cliffs, USA, 2002, pp. 461-463.



References Weight

Web of Science® Citations for all references: 2,532 TCR
SCOPUS® Citations for all references: 1,822 TCR

Web of Science® Average Citations per reference: 115 ACR
SCOPUS® Average Citations per reference: 83 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 2018-07-19 11:29 in 92 seconds.




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


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