A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada

Renewable and Sustainable Energy Reviews

Volumn 27, Issue , 2013, Pages 20-29

  Bigdeli, Nooshin ^a   Afshar, Karim ^a   Gazafroudi, Amin Shokri ^a   Ramandi, Mostafa Yousefi ^a  

^a IMAM KHOMEINI INTERNATIONAL UNIVERSITY   (Iran)

Author keywords

Genetic algorithm (GA); Imperialist competitive algorithm (ICA); Neural networks Particle swarm optimization (PSO); Recurrence plots; Time series analysis

Indexed keywords

HYBRID PREDICTION MODELS; IMPERIALIST COMPETITIVE ALGORITHM (ICA); MODELING AND PREDICTIONS; OPTIMIZATION APPROACH; PARTICLE SWARM OPTIMIZATION APPROACHES; RECURRENCE PLOT; WIND POWER FORECASTING; WIND POWER PREDICTIONS;

ELECTRIC POWER GENERATION; ELECTRIC UTILITIES; GENETIC ALGORITHMS; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); TIME SERIES ANALYSIS; WIND POWER;

WEATHER FORECASTING;

EID: 84880361986     PISSN: 13640321     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rser.2013.06.022     Document Type: Review

References (60)

1. PL Johnson, M Negnevitsky, and KM. Muttaqi Short term wind power forecasting using adaptive neuro-fuzzy inference systems Aust Univ Power Eng Conf (AUPEC) 2007 1 6
2. MA Ortega-Vazquez, and DS. Kirschen Estimating the spinning reserve requirements in systems with significant wind power generation penetration IEEE Trans Power Syst 24 1 2009 114 124
3. N Amjady, F Keynia, and HR. Zareipour A new hybrid iterative method for short-term wind speed forecasting Eur Trans Electr Power 21 2011 581 595
4. S Fan, JR Liao, R Yokoyama, L Chen, and WJ. Lee Forecasting the wind generation using a two-stage network based on meteorological information IEEE Trans Energy Convers 24 2 2009 474 482
5. RJ Bessa, V Miranda, and J. Gama Entropy and correntropy against minimum square error in offline and online three-day ahead wind power forecasting IEEE Trans Power Syst 24 4 2009 1657 1666
6. J Kabouris, and FD. Kanellos Impacts of large-scale wind penetration on designing and operation of electric power systems IEEE Trans Sustainable Energy 1 2 2010 107 114
7. B Ernst, B Oakleaf, ML Ahlstrom, M Lange, C Moehrlen, and B Lange Predicting the wind IEEE Power Energy Mag 5 6 2007 78 89 (Pubitemid 350121780)
8. N Amjady, F Keynia, and HR. Zareipour Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization IEEE Trans Sustainable Energy 2 3 2011 265 276
9. E Atashpaz-Gargari, and C. Lucas Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition IEEE Cong Evol Comp (CEC) 2007 4661 4667 (Pubitemid 351372009)
10. DE. Goldberg Genetic algorithms in search optimization and machine learning 1989 Addison Wesley Boston
11. D Sedighizadeh, and E. Masehian Particle swarm optimization methods, taxonomy and applications Int J Comp Theor Eng 1 5 2009 1793 8201
12. Eberhart RC, Kennedy J. Particle swarm optimization, in: Proceeding of the IEEE International Conference on Neural Networks. Perth Aust IEEE Serv Cent 1995, pp. 12-13.
13. Sideratos G, Hatziargyriou N. Using radial basis neural networks to estimate wind power production. IEEE Power Eng Soc Gen Meet 2007, 10.1109/PES.2007.385812. (Pubitemid 351580593)
14. A Costa, A Crespo, J Navarro, G Lizcano, H Madsen, and E. Feitosa A review on the young history of the wind power short-term prediction Renew Sustainable Energy Rev 12 6 2008 1725 1744 (Pubitemid 351626911)
15. M Lei, L Shiyan, J Chuanwen, L Hongling, and Z. Yan A review on the forecasting of wind speed and enerated power Renew Sustainable Energy Rev 13 4 2009 915 920
16. IJ Ramirez-Rosado, LA Fernandez-Jimenez, C Monteiro, J Sousa, and R. Bessa Comparison of two new short-term wind-power forecasting systems Renew Energy 34 7 2009 1848 1854
17. L Ma, SY Luan, CW Jiang, HL Liu, and Y. Zhang A review on the forecasting of wind speed and generated power Renew Sustainable Energy Rev 13 4 2009 915 920
18. N Amjady, F Keynia, and H. Zareipour Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization IEEE Trans Sustainable Energy 2 3 2011 265 276
19. Y Liu, J Shi, Y Yang, and WJ. Lee Short-term wind-power prediction based on wavelet transform-support vector machine and statistic-characteristics analysis IEEE Trans Ind App 48 2 2012 1136 1141
20. M Khalid, and AV. Savkin A method for short-term wind power prediction with multiple observation points IEEE Trans Power Syst 27 2 2012 579 586
21. X An, D Jiang, C Liu, and M. Zhao Wind farm power prediction based on wavelet decomposition and chaotic time series Exp Syst Appl 2011 11280 11285 38(9) 2011 11280 11285
22. A Kusiak, and W. Li Short-term prediction of wind power with a clustering approach Renew Energy 35 10 2010 2362 2369
23. G Yan, Y Liu, G Mu, Y Cui, J Li, and J Liu The ultra-short term prediction of wind power based on chaotic time series Energy Proc 2012 1490 1496 17(B) 2012 1490 1496
24. S Han, J Li, and Y. Liu Tabu search algorithm optimized ANN model for wind power prediction with NWP Energy Proc 12 2011 733 740
25. HMI Pousinho, and VMF Mendes Catalão JPS. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal Energy Conver and Manage 52 1 2011 397 402
26. B Zhu, MY Chen, N Wade, and L. Ran A prediction model for wind farm power generation based on fuzzy modeling Proc Env Sci 12(A) 2012 122 129
27. H Peng, F Liu, and X. Yang A hybrid strategy of short term wind power prediction Renew Energy 50 2013 590 595
28. AM Foley, PG Leahy, A Marvuglia, and EJ. McKeogh Current methods and advances in forecasting of wind power generation Renew Energy 37 1 2012 1 8
29. I Colak, S Sagiroglu, and M. Yesilbudak Data mining and wind power prediction: a literature review Renew Energy 46 2012 241 247
30. MG Giorgi, A Ficarella, and M. Tarantino Error analysis of short term wind power prediction models Appl Energy 88 4 2011 1298 1311
31. N Bigdeli, M Haeri, S Choobkar, and F. Jannesari Characterization of complex behaviors of TCP/RED computer networks based on nonlinear time series analysis methods Physica D: Nonlinear Phenom 233 2 2007 138 150 (Pubitemid 47284479)
32. N Bigdeli, and M. Haeri Time-series analysis of TCP/RED computer networks, an empirical study Chaos Solitons Fractals 39 2 2009 784 800
33. J Timmer, S Hässier, M Lauk, and CH. Lücking Pathological tremors: deterministic chaos or nonlinear stochastic oscillators Chaos 10 1 2000 278 288
34. Bigdeli N, Sadegh Lafmejani H, Afshar K. Characterization and short-term predictability analysis of wind speed and wind power time series. J Wind Eng Indust Aerodyn 2013; Submitted for publication.
35. J-P Eckmann, SO Kamphorst, D Ruelle, and S. Ciliberto Liapunov exponents from time series Phys Rev A 34 1986 4971 4979
36. HG. Schuster Handbook of chaos control 1999 Wiley VCH Singapore
37. H Casrellini, and L. Romanelli Application of recurrence quantified analysis to study the dynamics of chaotic chemical reaction Physica A 342 2004 301 307
38. KC Andrade, R Wehrle, VI Spoormaker, PG Samann, and M. Czisch Statistical evaluation of recurrence quantification analysis applied on single trial evoked potential studies Clinic Neurophy 123 8 2012 1523 1535
39. G Aschero, and P. Gizdulich Denoising of surface EMG with a modified Wiener filtering approach J Electromyog Kines 20 2 2010 366 373
40. WW Ng, US Panu, and WC. Lennox Chaos based analytical techniques for daily extreme hydrological observations J Hydro 342 2007 17 41 (Pubitemid 47069354)
41. P Armienti, and D. Gasperini Isotopic evidence for chaotic imprint in upper mantle heterogeneity Geochem Geophys Geosyst 11 5 2010 Q0AC02
42. N Bigdeli, M Jafarzadeh, and K. Afshar Characterization of Iran stock market indices using recurrence plots Proc Int Conf Manage Serv Sci (MASS) 2011 1 5
43. N Bigdeli, K. Afshar, and N. Amjady Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism Electr Power Syst Res 79 2009 888 898
44. N Bigdeli, and K. Afshar Chaotic behavior of price in the power markets with pay-as-bid payment mechanism Chaos Solitions Fractals 42 4 2009 2560 2569
45. N Bigdeli, and K. Afshar Characterization of Iran electricity market indices with pay-as-bid payment mechanism Physica A 388 8 2009 1577 1592
46. R Hegger, H Kantz, and T. Schreiber Practical implementation of nonlinear time series methods: the TISEAN package Chaos 9 1999 413 438
47. MC Marwan, M Romano, M Thiel, and J. Kurths Recurrence plots for the analysis of complex systems Phys Rep 438 2007 237 329
48. MC Romano, M Thiel, J Kurths, and W. Bloh Multivariate recurrence plots Phys Lett A 330 2004 214 223
49. CRP toolbox for Matlab provided by TOCSY. Available from: 〈http://tocsy.agnld.uni-potsdam.deâŒ, March 2013.
50. JPS Catalão, Mariano SJPS, and VMF Mendesb Ferreira LAFM. Short-term electricity prices forecasting in a competitive market: a neural network approach Electr Power Syst Res 77 2007 1297 1304 (Pubitemid 46813211)
51. S. Haykin Neural Networks: A Comprehensive Foundation 1999 Prentice-Hall New Jersey
52. LB. Almeida E Fiesler, R Beale, Multilayer perceptrons, handbook of neural computation 1997 Oxford University Press New York
53. BR Szkuta, LA Sanabria, and TS. Dillon Electricity price short-term forecasting using artificial neural networks IEEE Trans Power Syst 14 3 1999 851 857
54. M Abdechiri, K Faez, and H. Bahrami Neural network learning based on chaotic imperialist competitive algorithm 2nd Int Work Intel Syst Appl (ISA) 2010 1 5
55. T Niknam, E Taherian Fard, N Pourjafarian, and A. Rousta An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering Eng Appl Art Intel 24 2 2011 306 317
56. F Jolai, M Sangari, and M. Babaie Pareto simulated annealing and colonial competitive algorithm to solve an offline scheduling problem with rejection J Eng Manuf 224 7 2010 1119 1131
57. C Lucas, Z Nasiri-Gheidari, and F. Tootoonchian Application of an imperialist competitive algorithm to the design of a linear induction motor Energy Conver Manage 51 7 2010 1407 1411
58. Tayefeh Mahmoudi M, Forouzideh N, Lucas C, Taghiyareh F. Artificial neural network weights optimization based on imperialist competitive algorithm. 7th Int Conf Comp Sci Inf Tech 2009.
59. M Shahidehpour, H Yamin, and Z. Li Market operations in electric power systems: forecasting, scheduling, and risk management 2002 John Wiley & Sons Inc New York
60. The MathWorks, MATLAB. Available from: http://www.mathworks.com, March 2013.