Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine

Renewable and Sustainable Energy Reviews

Volumn 27, Issue , 2013, Pages 613-621

  Yan, Jie ^a   Liu, Yongqian ^a   Han, Shuang ^a   Qiu, Meng ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Grouping forecasts; Optimized relevance vector machine; Uncertainty analysis; Wind power

Indexed keywords

ELECTRIC UTILITIES; ITERATIVE METHODS; LEARNING SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO); VECTORS; WEATHER FORECASTING; WIND POWER; WIND TURBINES;

ERROR DISTRIBUTIONS; FORECASTING ACCURACY; FORECASTING MODELING; INTERVAL FORECASTING; LARGE-SCALE WIND FARMS; PREDICTION ACCURACY; PROBABILISTIC LEARNING; RELEVANCE VECTOR MACHINE;

UNCERTAINTY ANALYSIS;

EID: 84881341396     PISSN: 13640321     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rser.2013.07.026     Document Type: Review

References (44)

1. JW Taylor, PE McSharry, R Yokyama, L Chen, and W-J Lee Forecasting the wind generation using a two-stage network based on meteorological information IEEE Transactions on Energy Conversion 24 2 2009 474 482
2. J Usaola Probabilistic load flow with correlated wind power injections Electric Power Systems Research 80 5 2010 528 536
3. J Lerner, M Grundmeyer, and M Garvert. The importance of wind forecasting Renewable Energy Focus 10210 2 2009 64 66
4. YY Hong, HL Chang, and CS Chiu. Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs Energy 35 2010 3870 3876
5. MC Mabel, and E Fernandez Analysis of wind power generation and prediction using ANN: a case study Renewable Energy 33 5 2008 986 992
6. E Cadenas, and W Rivera Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks Renewable Energy 34 1 2009 274 278
7. IJ Ramirez-Rosado, LA Fernandez-Jimenez, C Monteiro, J Sousa, and R Bessa Comparison of two new short-term wind-power forecasting systems Renewable Energy 34 7 2009 1848 1854
8. JPS Catalão, HMI Pousinho, and VMF Mendes. Short-term wind power forecasting in Portugal by neural networks and wavelet transform Renewable Energy 36 2011 1245 1251
9. Yongqian Liu, Jie Shi, Yongping Yang, and Shuang Han Piecewise support vector machine model for short-term wind-power prediction International Journal of Green Energy 6 2009 479 489
10. MA Mohandes, TO Halawani, S Rehman, and Ahmed A. Hussain Support vector machines for wind speed prediction Renewable Energy 29 2004 939 947 (Pubitemid 38242182)
11. Hassen Bouzgou, and Nabil Benoudjit Multiple architecture system for wind speed prediction Applied Energy 88 2011 2463 2471
12. Nima Amjady, Farshid Keynia, and Hamidreza Zareipour Short-term wind power forecasting using ridgelet neural network Electric Power Systems Research 81 12 Dec. 2011 2099 2107
13. M Lei, L Shiyan, J Chuanwen, L Hongling, and Z Yan A review on the forecasting of wind speed and generated power Renewable and Sustainable Energy Reviews 13 4 2009 915 920
14. ME Tipping. Sparse Bayesian learning and the relevance vector machine Journal of Machine Learning Research 1 2001 211 244
15. WM Yu, TH Du, K Lim. Comparison of the support vector machine and relevant vector machine in regression and classification problems [C]. In: Eighth international conference on control, automation, robotics and vision, vol. 2. Kunming: China; Dec. 6-9, 2004.p. 1309-1314. (Pubitemid 40893748)
16. C Bowd, FA Medeiros, and ZB Zhang Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements [J] Investigative Ophthalmology & Visual Science 46 4 2005 1322 1329 (Pubitemid 41685872)
17. A Widodo, EY Kim, JD Son, BS Yang, ACC Tan, and DS Gu Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine Expert Systems with Applications 36 2009 7252 7261
18. Duan Q, Zhao JG, Niu L, et al. Regression based on sparse Bayesian learning and the applications in electric systems [c]. In: Fourth international conference on natural computing. Jinan: China; Oct. 18-20, 2008. p. 106-111.
19. Q Duan, JG Zhao, and Y Ma Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting Electric Machines and Control 14 6 2010 34 38
20. Wahyu Caesarendra, Achmad Widodo, and Bo-Suk Yang Application of relevance vector machine and logistic regression for machine degradation assessment Mechanical Systems and Signal Processing 24 2010 1161 1171
21. JG Lou, JH Jiang, CY Shuai, R Zhang, and A Jin Software reliability prediction model based on relevance vector machine Intelligent Computing and Intelligent Systems, ICIS 2009 20-22 2009 229 233 (Nov.)
22. Lazar Lazic', Goran Pejanovic', and Momčilo Živković Wind forecasts for wind power generation using the eta model Renewable Energy 35 2010 1236 1243
23. Maria Grazia, De Giorgi, Antonio Ficarella, and Marco Tarantino Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods Energy 36 2011 3958 3978
24. LIU Wei Research on optimal dispatching of the wind farm cluster [D] 2011 Beijing Jiaotong University Beijing
25. Sørensen P, Cutululis NA. Wind farms' spatial distribution effect on power system reserves requirements. In: ISIE 2010-IEEE international symposium on industrial electronics. Bari, Italy: IEEE; 2010. p. 2505-2510.
26. Shimamura T, Yamashita D, Koyanagi K, Nakanishi Y, Yokoyama R. Evaluation of smoothing effect of wind power generator aggregation on power system operation. In: International conference on renewable energies and power quality (ICREPQ'13) Bilbao (Spain); 20th-22nd March, 2013.
27. M Asari, T Nanahara, T Maejima, K Yamaguchi, and T Sato. A study on smoothing effect on output fluctuation of distributed wind power generation IEEE Power & Energy Society 2002 938 943
28. Pei Li, Hadi Banakar, Ping-Kwan Keung, Hamed Golestani Far, and Boon-Teck Ooi Macromodel of spatial smoothing in wind farms IEEE Transactions on Energy Conversion 22 1 2007 119 128 (MARCH) (Pubitemid 46487484)
29. B Tarroja, F Mueller, and JD Eichman Spatial and temporal analysis of electric wind generation intermittency and dynamics Renewable Energy 36 12 2011 3424 3432
30. De HJES, Frunt J, Kling KL Mitigation of wind power fluctuations in smart grids. In: Innovation smart grid technologies conference Europe (ISGT Europe). Gothenburg, Sweden: IEEE; 2010.p. 1-8.
31. Li Zhi, Han Xueshan, and Han Li An ultra-short-term wind power forecasting method in regional grids Automation of Electric Power System 34 7 2010 90 94 (in Chinese)
32. Han Yu, Chang Liuchen. A study of the reduction of the regional aggregated wind power forecast error by spatial smoothing effects in the Maritime Canada. In: IEEE electrical power & energy conference. Halifax, NS: Canada; 2010. p. 942-947.
33. Efe, B, Unal, E, Mentes, S, Ozdemir, T. Turkey by using the WRF model coupled to WindSim. In: Renewable energy research and applications (ICRERA), 2012 international conference on 11-14 Nov; 2012.
34. Yang Zhiling, Liu Yongqian, Li Chengrong. Interpolation of missing wind data based on ANFIS, Renewable Energy, 36, 3, 993-998.
35. I G Damousis, M C Alexiadis, J B Theocharis, and P S Dokopoulos A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation IEEE Transactions on Energy Conversion 19 2 2004 352 361 (Jun.)
36. T Kohonen. The self-organizing map Proceedings of the IEEE 78 1990 1464 1480
37. Jie YAN. Research on uncertainty analysis method for wind power prediction and its application. Beijing; 2012.
38. Zhang Jin-Hua, Liu Yong-Qian, Tian De. The short-term wind power forecast based on phase-space reconstruction and neural networks. Journal of Digital Information Management 2013;11(1):40-45.
39. Han Shuang, Liu Yongqian, Yan Jie. Neural network ensemble method study for wind power prediction. In: Proceedings of 2011 Asia-Pacific power and energy engineering conference. Wuhan: China, March 25-28; 2011.
40. P Pinson, and G N Kariniotakis Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment IEEE Bologna PowerTech - Conference Proceedings 2 2003
41. A Fabbri, T Gomez San Roman, J Rivier Abbad, and V H M Quezada Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market IEEE Transactions on Power Systems 20 3 2005 1440 1446 (Pubitemid 41300957)
42. K Methaprayoon, C Yingvivatanapong, W J Lee, and J R Liao An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty IEEE Transactions on Industry Applications 43 6 2007 1441 1448 (November-December) (Pubitemid 350201129)
43. LI Li, LIU Yongqian, YANG Yongping, and HAN Shuang Short-term wind speed forecasting based on CFD Pre-calculated flow fields Proceedings of the CSEE 33 7 2013 27 32
44. L Landberg. A mathematical look at a physical power prediction model Wind Energy 1 1 1998 23 28