© The Institution of Engineering and Technology
Wind energy has been part of the fastest growing renewable energy sources and is clean and pollution-free. Wind energy has been gaining increasing global attention, and wind speed forecasting plays a vital role in the wind energy field. However, such forecasting has been demonstrated to be a challenging task due to the effect of various meteorological factors. This study proposes a hybrid forecasting model that can effectively provide preprocessing for the original data and improve forecasting accuracy. The developed model applies a genetic algorithm-adaptive particle swarm optimisation algorithm to optimise the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined in regard to the wind farms of eastern China. The forecasting performance demonstrates that the developed model is better than some traditional models (for example, back propagation, WNN, fuzzy neural network, and support vector machine), and its applicability is further verified by the paired-sample T tests.
References
-
-
1)
-
5. Cadenas, E., Rivera, W.: ‘Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model’, Renew. Energy, 2010, 35, (12), pp. 2732–2738 (doi: 10.1016/j.renene.2010.04.022).
-
2)
-
21. Yeh, J.-R., Sun, W.-Z., Shieh, J.-S., et al: ‘Intrinsic mode analysis of human heartbeat time series’, Ann. Biomed. Eng., 2010, 38, pp. 1337–1344 (doi: 10.1007/s10439-010-9939-z).
-
3)
-
9. Salcedo-Sanz, S., Pérez-Bellido, A.M., Ortiz-García, E.G., et al: ‘Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction’, Renew. Energy, 2009, 34, (6), pp. 1451–1457 (doi: 10.1016/j.renene.2008.10.017).
-
4)
-
22. Wu, Z., Huang, N.E.: ‘EEMD: a noise-assisted data analysis method, centre for ocean-land-atmosphere studies’, Tech. Rep., 2004, 193, (51), p. 51.
-
5)
-
8. Mabel, M.C., Fernández, E.: ‘Analysis of wind power generation and prediction using ANN: a case study’, Renew. Energy, 2008, 33, (5), pp. 986–992 (doi: 10.1016/j.renene.2007.06.013).
-
6)
-
29. Xue, W.: ‘Statistical analysi`s methods and applications of SPSS’ (Publishing House of Electronics Industry, Beijing, 2009, 2nd edn.) .
-
7)
-
27. Han, F., Ling, Q.H.: ‘A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information’, Appl. Math. Comput., 2008, 205, (2), pp. 792–798 (doi: 10.1016/j.amc.2008.05.025).
-
8)
-
19. Huang, N.E., Wu, M.L., Qu, W., et al: ‘Applications of Hilbert–Huang transform to non-stationary financial time series analysis’, Appl. Stoch. Models Bus. Ind., 2003, 19, pp. 245–268 (doi: 10.1002/asmb.501).
-
9)
-
10)
-
13. Ho, D.W.C., Zhang, P.A., Xu, J.: ‘Fuzzy wavelet networks for function learning’, IEEE Trans. Fuzzy Syst., 2001, 9, (1), pp. 200–211 (doi: 10.1109/91.917126).
-
11)
-
3. Kavasseri, R.G., Seetharaman, K.: ‘Day-ahead wind speed forecasting using f-ARIMA models’, Renew. Energy, 2009, 34, pp. 1388–1393 (doi: 10.1016/j.renene.2008.09.006).
-
12)
-
18. Huang, N.E., Shen, Z., Long, S.R.: ‘A new view of nonlinear water waves: the Hilbert spectrum 1’, Annu. Rev. Fluid Mech., 1999, 31, pp. 417–457 (doi: 10.1146/annurev.fluid.31.1.417).
-
13)
-
25. Subasi, A., Alkan, A., Koklukaya, E., et al: ‘Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing’, Neural Netw., 2005, 18, pp. 985–997 (doi: 10.1016/j.neunet.2005.01.006).
-
14)
-
24. Ghohizadeh, S., Salajegheh, E., Torkzadeh, P.: ‘Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network’, J. Sound Vib., 2008, 312, pp. 316–331 (doi: 10.1016/j.jsv.2007.10.050).
-
15)
-
4. Mohandes, M., Halawani, T., Rehman, S., et al: ‘Support vector machines for wind speed prediction’, Renew. Energy, 2004, 29, pp. 939–947 (doi: 10.1016/j.renene.2003.11.009).
-
16)
-
28. Shi, Y., Eberhart, R.: ‘A modified particle swarm optimizer’. Proc. of the 1998 IEEE Int. Conf. on Evolutionary Computation Proc., IEEE World Congress on Computational Intelligence, 1998, pp. 69–73.
-
17)
-
6. Torres, J.L., García, A., De Blas, M., et al: ‘Forecast of hourly average wind speed with ARMA models in Navarre’, Sol. Energy, 2005, 79, (1), pp. 65–77 (doi: 10.1016/j.solener.2004.09.013).
-
18)
-
17. Haven, E., Liu, X.Q., Shen, L.: ‘De-noising option prices with the wavelet method’, Eur. J. Oper. Res., 2012, 222, pp. 104–112 (doi: 10.1016/j.ejor.2012.04.020).
-
19)
-
7. Flores, P., Tapia, A., Tapia, G.: ‘Application of a control algorithm for wind speed prediction and active power generation’, Renew. Energy, 2005, 30, (4), pp. 523–536 (doi: 10.1016/j.renene.2004.07.015).
-
20)
-
16. Guo, Z.H., Zhao, W.G., Lu, H.Y., et al: ‘Multi-step forecasting for wind speed using a modified empirical mode decomposition-based artificial neural network model’, Renew. Energy, 2012, 37, pp. 241–249 (doi: 10.1016/j.renene.2011.06.023).
-
21)
-
12. Sfetsos, A.: ‘A novel approach for the forecasting of mean hourly wind speed time series’, Renew. Energy, 2002, 27, (2), pp. 163–174 (doi: 10.1016/S0960-1481(01)00193-8).
-
22)
-
11. Cadenas, E., Rivera, W.: ‘Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks’, Renew. Energy, 2009, 34, (1), pp. 274–278 (doi: 10.1016/j.renene.2008.03.014).
-
23)
-
14. Zhang, Q., Benveniste, A.: ‘Wavelet networks’, IEEE Trans. Neural Netw., 1992, 3, pp. 889–898 (doi: 10.1109/72.165591).
-
24)
-
1. Salcedo-Sanz, S., Pérez-Bellido Ángel, M., Ortiz-García, E.G., et al: ‘Accurate short-term wind speed forecasting by exploiting diversity in input data using banks of artificial neural networks’, Neurocomputing, 2009, 72, pp. 1336–1341 (doi: 10.1016/j.neucom.2008.09.010).
-
25)
-
20. Lai, R.J., Huang, N.: ‘Investigation of vertical and horizontal momentum transfer in the gulf of Mexico using empirical mode decomposition method’, J. Phys. Oceanogr., 2005, 35, p. 1383 (doi: 10.1175/JPO2755.1).
-
26)
-
15. Sanner, M.R., Slotine, J.J.E.: ‘Structurally dynamic wavelet networks for adaptive control of robotic systems’, Int. J. Control, 1998, 70, (3), pp. 405–421 (doi: 10.1080/002071798222307).
-
27)
-
26. Kennedy, J., Eberhart, R.: ‘Particle swarm optimization’. Proc. of the IEEE Int. Conf. on Neural Networks, 1995, vol. 4, pp. 1942–1948.
-
28)
-
10. Monfared, M., Rastegar, H., Kojabadi, H.M.: ‘A new strategy for wind speed forecasting using artificial intelligent methods’, Renew. Energy, 2009, 34, pp. 845–848 (doi: 10.1016/j.renene.2008.04.017).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2015.0010
Related content
content/journals/10.1049/iet-rpg.2015.0010
pub_keyword,iet_inspecKeyword,pub_concept
6
6