|4/2010 - 17|
An advanced strategy for wind speed forecasting using expert 2-D FIR filtersMOGHADDAM, A. A. , SEIFI, A. R.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (2,239 KB) | Citation | Downloads: 1,245 | Views: 2,617|
energy forecasting, FIR filters, image processing, 2-D linear filtering, wind speed
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
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|
| B. Parsons, M. Milligan, B. Zavadil, D. Brooks, B. Kirby, K. Dragoon, and J. Caldwell, "Grid impacts of wind power: A summary of recent studies in the United States", in Proc. EWEC, Madrid, Spain, 2003.
 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 85] [SCOPUS Times Cited 101]
 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 45] [SCOPUS Times Cited 53]
 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 173] [SCOPUS Times Cited 244]
 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 36] [SCOPUS Times Cited 39]
 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 255] [SCOPUS Times Cited 363]
 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 253] [SCOPUS Times Cited 425]
 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 42] [SCOPUS Times Cited 64]
 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.
 L. Landberg, G. Giebel, H. A. Nielsen, T. Nielsen, and H. Madsen, "Short-term predictionAn 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 88] [SCOPUS Times Cited 102]
 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 177] [SCOPUS Times Cited 266]
 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.
 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.
 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.
 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 18]
 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.
 Intersil, "An introduction to digital filters", Application note, 1999, AN9603.2
 Scott C. Douglas, "Introduction to Adaptive Filters", Digital Signal Processing Handbook (1999) 7-12.
 Mark Nelson and Jean-Loup Gailly. "Speech Compression", The Data Compression Book (1995) 289-319.
 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 1009]
 Gonzalez, R. C., Woods, R. E. "Digital Image Processing", second ed. Prentice-Hall, Englewood Cliffs, USA, 2002, pp. 461-463.
Web of Science® Citations for all references: 2,181 TCR
SCOPUS® Citations for all references: 1,657 TCR
Web of Science® Average Citations per reference: 99 ACR
SCOPUS® Average Citations per reference: 75 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 2017-09-18 11:04 in 89 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.