Kernel ridge regression with active learning for wind speed prediction

Applied Energy

Volumn 103, Issue , 2013, Pages 328-340

  Douak, Fouzi ^a,b   Melgani, Farid ^a   Benoudjit, Nabil ^b  

^a UNIVERSITY OF TRENTO   (Italy)

^b Université de Batna ^*  (Algeria)

Author keywords

Active learning; Forecasting; Kernel ridge regression; Wind speed

Indexed keywords

FORECASTING; REGRESSION ANALYSIS; SAMPLING;

ACTIVE LEARNING; EXPERIMENTAL STUDIES; PREDICTION ERRORS; PREDICTION PROCESS; RIDGE REGRESSION; STRATEGY USE; TRAINING SAMPLE; WIND SPEED; WIND SPEED MEASUREMENT; WIND SPEED PREDICTION;

WIND EFFECTS;

ERROR ANALYSIS; EXPERIMENTAL STUDY; OPTIMIZATION; REGRESSION ANALYSIS; SAMPLING; WIND VELOCITY;

ALGERIA;

EID: 84871712040     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2012.09.055     Document Type: Article

References (42)

1. Himri Y., Malik A.S., Boudghene Stambouli A., Himri S., Draoui B. Review and use of the Algerian renewable energy for sustainable development. Renew Sustain Energy Rev 2009, 13(6-7):1584-1591.
2. Stambouli A.B. Promotion of renewable energies in Algeria: strategies and perspectives. Renew Sustain Energy Rev 2011, 15:1169-1181.
3. Chellali F., Khellaf A., Belouchrani A., Recioui A. A contribution in the actualization of wind map of Algeria. Renew Sustain Energy Rev 2011, 15:933-1002.
4. Burton T., Sharpe D., Jenkins N., Bossanyi E. Wind energy handbook 2001, Wiley.
5. Lei M., Shiyan L., Chuanwen J., Hongling L., Yan Z. A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev 2009, 13(4):915-920.
6. Morales J.M., Minguez R., Conejo A.J. A methodology to generate statistically dependent wind speed scenarios. Appl Energy 2010, 87(3):843-855.
7. 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(3):724-732.
8. Erdem E., Shi J. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 2011, 88(4):1405-1414.
9. Lange M., Focken U. Physical approach to short term wind power prediction 2009, Springer-Verlag, New York, LLC.
10. Lei M., Jiang C., Liu H., Zhang Y. A review on forecasting wind data and wind output. Renew Sustain Energy Rev 2009, 13(4):915-920.
11. Fadare D.A. The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria. Appl Energy 2010, 87(3):934-942.
12. Li G., Shi J. On comparing three artificial neural networks for wind speed forecasting. Appl Energy 2010, 87(7):2313-2320.
13. Li G., Shi J. Application of Bayesian model averaging in modeling long-term wind speed distributions. Renew Energy 2010, 35(6):1192-1202.
14. Mohandes M., Rehman S., Rahman S.M. Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy 2011, 88(11):4024-4032.
15. Mohandes M.A., Halawani T.O., Rehman S., Hussain A.A. Support vector machines for wind speed prediction. Renew Energy 2004, 29(6):939-947.
16. Mohandes M.A., Rehman S., Halawani T.O. A neural networks approach for wind speed prediction. Renew Energy 1998, 13(3):345-354.
17. Kariniotakis G.N., Stavrakakis G.S., Nogaret E.F. Wind power forecasting using advanced neural networks models. IEEE Trans Energy Convers 1996, 11(4):762-767.
18. More A., Deo M.C. Forecasting wind with neural networks. Mar Struct 2003, 16(1):35-49.
19. Erto P., Lanzotti A., Lepore A. Wind speed parameter estimation from 1-month sample via Bayesian approach. Qual Rel Eng Int 2010, 26(8):853-862.
20. Bouzgou H., Benoudjit N. Multiple architecture system for wind speed prediction. Appl Energy 2011, 88(7):2463-2471.
21. Barbounis T.G., Theocharis J.B., Alexiadis M.C., Dokopoulos P.S. Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Trans Energy Convers 2006, 21(1):273-284.
22. Barbounis T.G., Theocharis J.B. A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation. Neurocomputing 2007, 70(7-9):1525-1542.
23. Barbounis T.G., Theocharis J.B. Locally recurrent neural networks for wind speed prediction using spatial correlation. Inform Sci 2007, 177(24):5775-5797.
24. Lackner MA, Rogers AL, Manwell JF. Uncertainty analysis in wind resource assessment and wind energy production estimation. In: 45th AIAA aerospace sciences meeting exhibit. Reno; 2007.
25. Seguro J.V., Lambert T.W. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. J Wind Eng Ind Aerod 2000, 85(1):75-84.
26. Akdag S.A., Dinler A. A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage 2009, 50(7):1761-1766.
27. Lange M. On the uncertainty of wind power predictions-analysis of the forecast accuracy and statistical distribution of errors. J Sol Energy Eng 2005, 127:177-184.
28. Lackner M.A., Rogers A.L., Manwell J.F. Uncertainty analysis in MCP-based wind resource assessment and energy production estimation. J Sol Energy Eng 2008, 130.
29. Milashuk S., Crane W.A. Wind speed prediction accuracy and expected errors of RANS equations in low relief inland terrain for wind resource assessment purposes. Environ Model Software 2011, 26(4):429-433.
30. Zhu X. Semi-Supervised Learning Literature Survey. In: Comput Sci Tech Rep 1530, Univ. Wisconsin-Madison; 2005.
31. Bazi Y., Melgani F. Semisupervised PSO-SVM regression for biophysical parameter estimation. IEEE Trans Geosci Remote Sens 2007, 45(6):1887-1895.
32. Bazi Y, Melgani F. Semisupervised Gaussian process regression for biophysical parameter estimation. In: Geosci remote sens symposium IGARSS 2010. p. 4248-51.
33. Settles B. Active learning literature survey. Comput Sci Tech Rep 1648. Univ Wisconsin-Madison; 2009.
34. Pasolli E., Melgani F. Active learning methods for electrocardiographic signal classification. IEEE Trans Inform Technol Biomed 2010, 14(6):1405-1416.
35. Pasolli E., Melgani F., Bazi Y. Support vector machine active learning through significance space construction. IEEE Geosci Remote Sens Lett 2011, 8(3):431-435.
36. Saunders C, Gammerman CA, Vovk V. Ridge regression learning algorithm in dual variables. In: Proc 15th Int conf mach. Learning; 1998. p. 515-21.
37. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction 2009, Springer, New York.
38. Orsenigo C., Vercellis C. Kernel ridge regression for out-of-sample mapping in supervised manifold learning. Expert Syst Appl 2012, 39(9):7757-7762.
39. Cuaresma J.C., Hlouskova J., Kossmeier S., Obersteiner M. Forecasting electricity spot-prices using linear univariate time-series models. Appl Energy 2004, 77(1):87-106.
40. Weigend A.S., Gershnfeld N.A. Time series prediction: forecasting the future and understanding the past 1994, Addison-Wesley Publishing Company.
41. Cristianini N., Taylor J.S. An introduction to support vector machines and other kernel-based learning methods 2000, Cambridge University Press.
42. Douak F., Benoudjit N., Melgani F. A two-stage regression approach for spectroscopic quantitative analysis. Chemom Intell Lab Syst 2011, 109(1):34-41.