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An advanced strategy for wind speed forecasting using expert 2-D FIR filtersMOGHADDAM, A. A. , SEIFI, A. R.
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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)
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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 95] [SCOPUS Times Cited 109]
 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 54]
 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 179] [SCOPUS Times Cited 253]
 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 37] [SCOPUS Times Cited 40]
 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 278] [SCOPUS Times Cited 384]
 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 287] [SCOPUS Times Cited 460]
 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]
 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 91] [SCOPUS Times Cited 106]
 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 192] [SCOPUS Times Cited 280]
 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 19]
 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 1109]
 Gonzalez, R. C., Woods, R. E. "Digital Image Processing", second ed. Prentice-Hall, Englewood Cliffs, USA, 2002, pp. 461-463.
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