Wind power grid connected capacity prediction using LSSVM optimized by the bat algorithm

Energies

Volumn 8, Issue 12, 2015, Pages 14346-14360

  Wu, Qunli ^a   Peng, Chenyang ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Bat algorithm (BA); Granger causality test; Least squares support vector machine (LSSVM); Wind power grid connected capacity prediction

Indexed keywords

ECONOMICS; ELECTRIC LOAD DISPATCHING; PLANNING; STATISTICAL TESTS; STOCHASTIC SYSTEMS; SUPPORT VECTOR MACHINES; WEATHER FORECASTING; WIND POWER;

ACCURACY IMPROVEMENT; BAT ALGORITHMS; GRANGER CAUSALITY TEST; GRID-CONNECTED CAPACITIES; LEAST SQUARES SUPPORT VECTOR MACHINES; PARTIAL AUTOCORRELATION; PREDICTION PERFORMANCE; WIND POWER GRIDS;

ELECTRIC POWER TRANSMISSION NETWORKS;

EID: 84952314900     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en81212428     Document Type: Article

References (34)

1. Xu, J.; He, D.; Zhao, X. Status and prospects of chinese wind energy. Energy 2010, 35, 4439-4444.
2. Foley, A.M.; Leahy, P.G.; Marvuglia, A.; McKeogh, E.J. Current methods and advances in forecasting of wind power generation. Renew. Energy 2012, 37, 1-8.
3. Jin, X.; Zhang, Z.; Shi, X.; Ju,W. A review on wind power industry and corresponding insurance market in china: Current status and challenges. Renew. Sustain. Energy Rev. 2014, 38, 1069-1082.
4. Zou, J.; Peng, C.; Yan, Y.; Zheng, H.; Li, Y. A survey of dynamic equivalent modeling for wind farm. Renew. Sustain. Energy Rev. 2014, 40, 956-963.
5. Ahmed, H. Reactive power and voltage control in grid-connected wind farms: An online optimization based fast model predictive control approach. Electr. Eng. 2015, 97, 35-44.
6. Ewing, B.T.; Kruse, J.B.; Schroeder, J.L.; Smith, D.A. Time series analysis of wind speed using var and the generalized impulse response technique. J. Wind Eng. Ind. Aerodyn. 2007, 95, 209-219.
7. Hill, D.; Bell, K.R.W.; McMillan, D.; Infield, D. A vector auto-regressive model for onshore and offshore wind synthesis incorporating meteorological model information. Adv. Sci. Res. 2014, 11, 35-39.
8. Torres, J.L.; García, A.; de Blas, M.; de Francisco, A. Forecast of hourly average wind speed with arma models in Navarre (Spain). Sol. Energy 2005, 79, 65-77.
9. Erdem, E.; Shi, J. Arma based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 2011, 88, 1405-1414.
10. Liu, H.; Erdem, E.; Shi, J. Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed. Appl. Energy 2011, 88, 724-732.
11. Jiang, W.; Yan, Z.; Feng, D.-H.; Hu, Z. Wind speed forecasting using autoregressive moving average/generalized autoregressive conditional heteroscedasticity model. Eur. Trans. Electr. Power 2012, 22, 662-673.
12. Tagliaferri, F.; Viola, I.M.; Flay, R.G.J.Wind direction forecasting with artificial neural networks and support vector machines. Ocean Eng. 2015, 97, 65-73.
13. Buhan, S.; Cadirci, I. Multistage wind-filectric power forecast by using a combination of advanced statistical methods. IEEE Trans. Ind. Inform. 2015, 11, 1231-1242.
14. Shukur, O.B.; Lee, M.H. Daily wind speed forecasting through hybrid kf-ann model based on arima. Renew. Energy 2015, 76, 637-647.
15. Ata, R. Artificial neural networks applications in wind energy systems: A review. Renew. Sustain. Energy Rev. 2015, 49, 534-562.
16. Petkovic, D.; Shamshirband, S.; Anuar, N.B.; Naji, S.; Kiah, M.L.M.; Gani, A. Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction. Stoch. Environ. Res. Risk Assess. 2015, 29, 793-802.
17. Zhang, Y.; Yang, J.; Wang, K.; Wang, Z. Wind power prediction considering nonlinear atmospheric disturbances. Energies 2015, 8, 475-489.
18. Guo, Z.H.; Zhao, J.; Zhang,W.Y.;Wang, J.Z. A corrected hybrid approach for wind speed prediction in Hexi Corridor of China. Energy 2011, 36, 1668-1679.
19. De Giorgi, M.; Campilongo, S.; Ficarella, A.; Congedo, P. Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 2014, 7, 5251-5272.
20. Yuan, X.; Chen, C.; Yuan, Y.; Huang, Y.; Tan, Q. Short-term wind power prediction based on LSSVM-GSA model. Energy Convers. Manag. 2015, 101, 393-401.
21. Wang, X.; Li, H. One-month ahead prediction of wind speed and output power based on EMD and LSSVM. In Proceedings of the International Conference on Energy and Environment Technology, Guilin, China, 16-18 October 2009; pp. 439-442.
22. Hu, Z.Y.; Liu, Q.Y.; Tian, Y.X.; Liao, Y.F. A short-term wind speed forecasting model based on improved QPSO optimizing LSSVM. In Proceedings of the International Conference on Power System Technology (POWERCON), Chengdu, China, 20-22 October 2014.
23. Sun, B.; Yao, H.T. The short-term wind speed forecast analysis based on the PSO-LSSVM predict model. Power Syst. Prot. Control 2012, 40, 85-89.
24. Wang, J.Z.;Wang, Y.; Jiang, P. The study and application of a novel hybrid forecasting model-A case study of wind speed forecasting in china. Appl. Energy 2015, 143, 472-488.
25. Song, Z.; Li, J. Prediction model based on least squares support vector machine with harmony search and its application. J. Harbin Inst. Technol. 2009, 41, 208-210.
26. Yang, X.-S. A new metaheuristic bat-inspired algorithm. Nat. Inspir. Coop. Strateg. Optim. 2010, 284, 65-74.
27. Hafezi, R.; Shahrabi, J.; Hadavandi, E. A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of dax stock price. Appl. Soft Comput. 2015, 29, 196-210.
28. Meng, X.B.; Gao, X.Z.; Liu, Y.; Zhang, H.Z. A novel bat algorithm with habitat selection and doppler effect in echoes for optimization. Expert Syst. Appl. 2015, 42, 6350-6364.
29. Senthilkumar, P.; Vanitha, N.S. A unified approach to detect the record duplication using bat algorithm and fuzzy classifier for health informatics. J. Med. Imaging Health Inform. 2015, 5, 1121-1132.
30. Rao, B.V.; Nagesh Kumar, G.V. Optimal power flow by bat search algorithm for generation reallocation with unified power flow controller. Int. J. Electr. Power Energy Syst. 2015, 68, 81-88.
31. Yang, N.C.; Le, M.D. Optimal design of passive power filters based on multi-objective bat algorithm and pareto front. Appl. Soft Comput. 2015, 35, 257-266.
32. Tang, C.F.; Tan, E.C. Electricity consumption and economic growth in Portugal: Evidence from a multivariate framework analysis. Energy J. 2012, 33, 23-48.
33. Suykens, J.A.K.; Vandewalle, J. Recurrent least squares support vector machines. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 2000, 47, 1109-1114.
34. Ahmed, H.; Ushirobira, R.; Efimov, D.; Tran, D.; Sow, M.; Payton, L.; Massabuau, J.C. A fault detection method for automatic detection of spawning in oysters. IEEE Trans. Control Syst. Technol. 2015.