Performance comparison of multilayer perceptrons and support vector machines in a short-term wind speed prediction problem

Neural Network World

Volumn 19, Issue 1, 2009, Pages 37-51

  Salcedo Sanz, S ^a   Ortiz Garcia E G ^a   Perez Bellido A M ^a   Portilla Figueras, A ^a   Prieto, L ^b   Paredes, D ^c   Correoso, F ^c  

^a UNIVERSITY OF ALCALÁ   (Spain)

^b IBERDROLA   (Spain)

^c UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Down scaling processes; Global forecasting models; Multilayer perceptrons; Support vector machines; Wind speed forecast

Indexed keywords

CYBERNETICS; ELECTRIC LOAD FORECASTING; ELECTRIC LOADS; ELECTRIC UTILITIES; FARMS; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; MULTILAYERS; PACKET NETWORKS; PATTERN RECOGNITION SYSTEMS; SPEED; TURBINES; VECTORS; WEATHER FORECASTING; WIND EFFECTS; WIND POWER;

ATMOSPHERIC SOUNDINGS; DIFFERENT HEIGHTS; DOWN-SCALING PROCESSES; FINAL OBJECTIVES; GLOBAL FORECASTING MODELS; GLOBAL MODELS; MESO-SCALE; METEOROLOGICAL PREDICTIONS; MULTILAYER PERCEPTRONS; PERFORMANCE COMPARISONS; PREDICTION MODELS; PREDICTION SYSTEMS; SHORT-TERM PREDICTIONS; TOTAL POWER; WEATHER FORECASTING MODELS; WIND FARMS; WIND SPEED; WIND SPEED FORECAST; WIND SPEED PREDICTIONS;

SUPPORT VECTOR MACHINES;

EID: 63149182207     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (32)

1. Mabel M. C., Fernández E.: Analysis of wind power generation and prediction using ANN: A case study, Renewable Energy, 33, 5, 2008, pp. 986-992.
2. Vapnik V. N.: Statistical Learning Theory, In Adaptive and Learning Systems for Signal Processing, Communications and Control, S. Haykin, Ed. John Wiley and Sons, 1998.
3. Smola A. J., Schölkopf B.: A tutorial on support vector regression, Statistics and Computing, 1998.
4. Haykin S.: Neural network: a compronhensive foundation, Prentice Hall, 1998.
5. Bishop C. M.: Neural networks for pattern recognition, Oxford University Press, 1995.
6. Hagan M. T., Menhaj M.: Training feed-forward networks with the Levenberg-Marquardt algorithm, IEEE Transactions on Neural Networks, 5, 6, 1994, pp. 989-993.
7. Salcedo-Sanz S., Camps-Vails G., Prez-Cruz F., Sepúlveda- Sanchís J., Bousoño-Calzón C.: Enhancing genetic feature selection through restricted search and Walsh analysis, IEEE Transactions on Systems, Man and Cybernetics, Part C, 34, 4, 2004, pp. 398-406.
8. Chang C. C., Lin C. J.: Libsvm: a library for support vector machines, 2001, available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
9. Toll Jr. R. F., Clune W. M.: An operational evaluation of the Navy Operational Global Atmospheric Prediction System (NOGAPS): 48-hour surface pressure forecasts, Monthly Weather Review, 113, 9, 1995, pp. 1433-1440.
10. GFS (NOMADS) global forecasting model, http://nomad5.ncep.noaa.gov/
11. GFS data, ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/gfs/
12. Metar data, ftp://tgftp.nws.noaa.gov/data/observations/metar/stations/
13. Soundings data, http://weather.uwyo.edu/upperair/sounding.html/
14. Goh S. L., Chen M., Popovic D. H., Aihara K., Obradovic D., Mandic D. P.: Complex-valued forecasting of wind profile, Renewable Energy, 31, 2006, pp. 1733-1750.
15. Mohandes M. A., Halawani T. O., Rehman S., Hussain A. A.: Support vector machines for wind speed prediction, Renewable Energy, 29, 2004, pp. 939-947.
16. Cadenas E., Rivera W.: Wind speed forecasting in the south coast of Oaxaca, Mexico, Renewable Energy, 32, 2007, pp. 2116-2128.
17. Bilgili M., Sahin B., Yasar A.: Application of artificial neural networks for the wind speed prediction of target stations using reference stations data, Renewable Energy, 32, 2007, pp. 2350-2360.
18. ter Borg R. W., Rothkrantzy L. J.: Short-term wind power prediction with radial basis networks, Neural Network World, 16, 2, 2006, pp. 151-161.
19. Flores P., Tapia A., Tapia G.: Application of a control algorithm for wind speed prediction and active power generation, Renewable Energy, 30, 2005, pp. 523-536.
20. Bechrakis D. A., Sparis P. D.: Correlation of wind speed between neighboring measuring stations, IEEE Transactions on Energy Conversion, 19, 2004, pp. 400-406.
21. Karioniotakis G. S., Nogaret E. F.: Wind power forecasting using advanced neural networks models, IEEE Transactions on Energy Conversion, 11, 4, 1996, pp. 762-767.
22. Li S., Wunch C. D., O'Hair A., Giesselmann G. M.: Using neural networks to estimate wind turbine power generation, IEEE Transactions on Energy Conversion, 16, 3, 2001, pp. 276-281.
23. Pepper D. W., Wang X.: Application of an h-adaptive finite element model for wind energy assessment in Nevada, Renewable Energy, 32, 10, 2007, pp. 1705-1722.
24. Zhang G. P., Qi M.: Neural network forecasting for seasonal and trend time series, European Journal of Operational Research, 160, 2, 2005, pp. 501-514.
25. Barbounis T. G., Theocharis J. B.: Locally recurrent neural networks for wind speed prediction using spatial correlation, Information Sciences, 177, 2007, pp. 5775-5797.
26. Landberg L.: Short-term prediction of the power production from wind farms, Journal of Wind Engineering and Industrial Aerodynamics, 80, 1999, pp. 207-220.
27. Landberg L.: Short-term prediction of local wind conditions, Journal of Wind Engineering and Industrial Aerodynamics, 89, 2001, pp. 235-245.
28. Salcedc-Sanz S., Pérez-Bellido A. M., Ortiz-García E. G., Portilla-Figueras A., Prieto L., Paredes D., Correoso F.: Short-term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks, IEEE Conference on Hybrid Intelligent Systems, Barcelona, Spain, 2008.
29. Dudhia J.: A nonhydrostatic version of the Penn State-NCAR Mesoscale Model: validation, tests and simulation of an atlantic cyclone and cold front, Monthly Weather Review, 121, 1993, pp. 1493-1513.
30. San José R., Pérez J. L., González R. M.: The evaluation of the air quality impact of an incinerator by using MM5-CMAQ-EMIMO modeling system: North of Spain case study, Environment International, 34, 5, 2008, pp. 714-719.
31. Mellit A., Kalogirou S. A.: Artificial intelligence techniques for photovoltaic applications: A review, Progress in Energy and Combustion Science, 34, 5, 2008, pp. 574-632.
32. Khashei M., Bijari M., Raissi-Ardali G.: Improvement of Auto-Regressive Integrated Moving Average Models Using Fuzzy Logic and Artificial Neural Networks (ANNs), Neurocomputing, in press, 2008.