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AUTHOR(S):

Cuicui Li, Ying Huang, Yang Zhao

 

TITLE

A novel wind speed prediction method based on support vector machine optimized by genetic algorithm

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ABSTRACT

Wind energy is attracting more and more attention nowadays, because of the renewable, nonpolluting characteristics. Accurate wind speed prediction can provide necessary technical support and guidance in the application of wind power in the electricity grid. In order to improve the accuracy of wind speed prediction, a combination genetic algorithm and support vector machine (SVM) model has been proposed to forecast wind speed. Firstly, grey model was adopted to accumulate the original wind speed data and weaken the randomness of data sequence, and then SVM model is used to predict the wind speed. Furthermore, using the regressive features of grey model to reduce the prediction results and obtain the final predicted value of wind speed. The comparison results with other popular predicting algorithms, BP and standard SVM, show that, the GA-SVM model can improve the forecasting accuracy of short term wind speed and is of a certain practical value.

KEYWORDS

Wind energy, Wind speed prediction, Support vector machine (SVM), genetic algorithm (GA)

REFERENCES

[1] S.F. Khahro, K. Tabbassum, A.M. Soomro, L. Dong, X. Liao, “Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan”, Energy Conversion and Management, vol. 78, (2014) February, pp. 956-967.

[2] A.M. Borchers, I. Xiarchos, J. Beckman, “Determinants of wind and solar energy system adoption by U.S. farms: a multilevel modeling approach”, Energy Policy, vol. 69, (2014) June, pp. 106-115.

[3] A. Mostafaeipour, “Economic evaluation of small wind turbine utilization in Kerman, Iran”, Energy Conversion and Management, vol. 73, (2013) September, pp.214-225.

[4] R. Velo, P. López, F. Maseda, “Wind speed estimation using multilayer perceptron”, Energy Conversion and Management, vol. 81, (2014) May, pp. 1-9.

[5] Z. Guo, D. Chi, J. Wu, W. Zhang, “A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm”, Energy Conversion and Management, vol. 84, (2014) August, pp. 140-151.

[6] M. Yesilbudak, S. Sagiroglu, I. Colak, “A new approach to very short term wind speed prediction using k-nearest neighbor classification”, Energy Conversion and Management, vol. 69, (2013) May pp. 77-86.

[7] D. Petkovic, S. Shamshirband, N.B. Anuar, H. Saboohi, et al, “An appraisal of wind speed distribution prediction by soft computing methodologies: a comparative study”, Energy Conversion and Management, vol. 84 (2014) August, pp. 133-139.

[8] J.L. Torres, A. García, M.D. Blas, et al, “Forecast of hourly average wind speed with ARMA models in Navarre”, Solar Energy, vol. 79, no. 1, (2005) July, pp. 65-77.

[9] P. Louka, G. Galanis, N. Siebert, et al, “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering”, Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 12, (2008) March, pp. 2348-2362.

[10] P. Louka, G. Galanis, N. Siebert, et al, “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering”, Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 12, (2008) December, pp. 2348-2362.

[11] J.L. Torres, A. García, M.D. Blas, et al, “Forecast of hourly average wind speed with ARMA models in Navarre”, Solar Energy, vol. 79, no. 1, (2005) July, pp. 65-77.

[12] J.A. Carta, S. Velázquezb, J.M. Matías, “Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site”. Energy Conversion and Management, vol. 52, no. 2, (2011) February, pp. 1137-1149.

[13] K. G. Sheela, S.N. Deepa, “Neural network based hybrid computing model for wind speed prediction”, Neurocomputing, vol. 122, no. 25, (2013) December, pp. 425-429.

[14] T. G. Barbounis, J.B. Theocharis, “A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation”, Neurocomputing vol. 70, no. 7-9, (2007) March, pp. 1525-1542.

[15] G. Grassi, P. Vecchio, “Wind energy prediction using a two-hidden layer neural network”, Wind energy prediction using a two-hidden layer neural network, vol. 15, no. 9, (2010) September, pp. 2262- 2277.

[16] B. S. Morenoa, S. S. Sanza, L. C. Calvoa, J. G. Morenoa, S. J. Fernándeza, L. Prietob, “Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms”, Journal of Wind Engineering and Industrial Aerodynamics, vol. 116, (2013) May, pp. 49-60.

[17] M. Serinivas, L.M. Patnaik, “Adaptive probabilities of crossover and mutation in genetic algorithms”, IEEE Trans on Systems, Man and Cybernetics, vol. 24, no. 4, (1994) pp. 656-667.

Cite this paper

Cuicui Li, Ying Huang, Yang Zhao. (2017) A novel wind speed prediction method based on support vector machine optimized by genetic algorithm. International Journal of Mathematical and Computational Methods, 2, 412-418

 

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