Predicting of the short term wind speed by using a real valued genetic algorithm based least squared support vector machine

Smart Innovation, Systems and Technologies

Volumn 10 SIST, Issue , 2011, Pages 567-575

  Huang, Chi Yo ^a   Chiang, Bo Yu ^b   Chang, Shih Yu ^b   Tzeng, Gwo Hshiung ^c,d   Tseng, Chun Chieh ^e  

^a NATIONAL TAIWAN NORMAL UNIVERSITY   (Taiwan)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^c KAINAN UNIVERSITY   (Taiwan)

^d NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^e TAIWAN POWER COMPANY   (Taiwan)

Author keywords

Genetic algorithm (GA); Least squared support vector machine (LS SVM); Short term wind prediction; Support vector machines (SVMs); Wind power; Wind speed forecasting

Indexed keywords

ALTERNATIVE ENERGY; ALTERNATIVE ENERGY SOURCE; DATA SETS; EMPIRICAL STUDIES; HUMAN BEING; LEAST SQUARED SUPPORT VECTOR MACHINE (LS-SVM); POSSIBLE FUTURES; PREDICTION ERRORS; REAL-VALUED GENETIC ALGORITHM; SHORT TERM; TIME SPAN; TURBINE SPEED; WIND APPLICATIONS; WIND FARM; WIND FORECASTS; WIND PREDICTION; WIND SPEED; WIND SPEED FORECASTING;

ELECTRIC UTILITIES; FOSSIL FUELS; GENETIC ALGORITHMS; WEATHER FORECASTING; WIND EFFECTS; WIND POWER;

SUPPORT VECTOR MACHINES;

EID: 84866334911     PISSN: 21903018     EISSN: 21903026     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-642-22194-1_56     Document Type: Conference Paper

References (24)

1. Adewuya, A.A.: New Methods in Genetic Search with Real-Valued Chromosomes: Massachusetts Institute of Technology, Dept. of Mechanical Engineering (1996)
2. Carolin Mabel, M., Fernandez, E.: Analysis of wind power generation and prediction using ANN: A case study. Renewable Energy 33(5), 986-992 (2008)
3. Catalao, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renewable Energy 36(4), 1245-1251 (2011)
4. 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. Renewable and Sustainable Energy Reviews 12(6), 1725-1744 (2008)
5. Damousis, I.G., Alexiadis, M.C., Theocharis, J.B., Dokopoulos, P.S.: A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Transactions on Energy Conversion 19(2), 352-361 (2004)
6. Giebel, G., Landberg, L., Kariniotakis, G., Brownsword, R.: State-of-the-art on methods and software tools for short-term prediction of wind energy production. In: European Wind Energy Conference, Madrid (2003)
7. Global Wind Energy Council. Global wind capacity increases by 22% in 2010 - Asia leads growth (2011)
8. Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Pub. Co., Reading (1989)
9. Huang, Y.-P., Huang, C.-H.: Real-valued genetic algorithms for fuzzy grey prediction system. Fuzzy Sets and Systems 87(3), 265-276 (1997), doi:10.1016/s0165-0114(96)00011-5
10. Landberg, L.: Short-term prediction of the power production from wind farms. Journal of Wind Engineering and Industrial Aerodynamics 80(1-2), 207-220 (1999)
11. Lerner, J., Grundmeyer, M., Garvert, M.: The importance of wind forecasting. Renewable Energy Focus 10(2), 64-66 (2009)
12. Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.G.: Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2(1) (2009)
13. Ma, L., Luan, S., Jiang, C, Liu, H., Zhang, Y.: A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13,915-920 (2009)
14. Marciukaitis, M., Katinas, V., Kavaliauskas, A.: Wind power usage and prediction prospects in Lithuania. Renewable and Sustainable Energy Reviews 12, 265-277 (2008)
15. Mathew, S.: Wind energy: fundamentals, resource analysis and economics. Springer, Heidelberg (2006)
16. Muñoz, M., Oschmann, V., David Tabara, J.: Harmonization of renewable electricity feed-in laws in the European Union. Energy Policy 35(5), 3104-3114 (2007)
17. Sfetsos, A.: A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21(1), 23-35 (2000)
18. Soman, S.S., Zareipour, H., Malik, O., Mandal, P.: 2010. A review of wind power and wind speed forecasting methods with different time horizons. In: North American Power Symposium, NAPS (2010)
19. Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9(3), 293-300 (1999)
20. Vapnik, V.N.: Statistical learning theory. Wiley, Chichester (1998)
21. Vapnik, V.N.: The nature of statistical learning theory. Springer, Heidelberg (2000)
22. Wu, C.-H., Tzeng, G.-H., Goo, Y.-J., Fang, W.-C: A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Systems with Applications 32(2), 397-408 (2007)
23. Wu, M.T.: The Application of Artificial Neural Network to Wind Speed and Generation Forecasting of Wind Power System. Department of Electrical Engineering, National Kaohsiung University of Application Sciences, Kaohsiung (2008)
24. Wu, Y.K., Hong, J.S.: A literature review of wind forecasting technology in the world. In: Power Tech. IEEE Lausanne (2007)