Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data

Renewable Energy

Volumn 92, Issue , 2016, Pages 202-211

  Doucoure, Boubacar ^a   Agbossou, Kodjo ^a   Cardenas, Alben ^a  

^a UNIVERSITÉ DU QUÉBEC À TROIS RIVIÈRES   (Canada)

Author keywords

Adaptive wavelet neural network; Multi resolution analysis; Wind speed forecasting

Indexed keywords

ALGORITHMS; COMPLEX NETWORKS; COMPUTATIONAL COMPLEXITY; FORECASTING; MULTIRESOLUTION ANALYSIS; NEURAL NETWORKS; RENEWABLE ENERGY RESOURCES; TIME SERIES; WAVELET DECOMPOSITION; WIND;

ADAPTIVE WAVELET NEURAL NETWORK; INTELLIGENT MANAGEMENT; ISOLATED POWER SYSTEM; PREDICTION ALGORITHMS; RENEWABLE ENERGY SOURCE; TIME SERIES PREDICTION; WAVELET NEURAL NETWORKS; WIND SPEED FORECASTING;

TIME SERIES ANALYSIS;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; DATA ASSIMILATION; PREDICTION; RENEWABLE RESOURCE; TIME SERIES ANALYSIS; WAVELET; WIND VELOCITY;

EID: 84957886411     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2016.02.003     Document Type: Article

References (44)

1. Freris L., Infield D. Renewable Energy in Power Systems 2008, A John Wiley & Sons, Ltd, Publication.
2. Sophia Antipolis (Banque européenne d'inverstissement) Etude sur le changement climatique et énergie en Mediterranée 2008, Tech. rep., Plan Bleu Centre d'Activités Régionales.
3. Ruud Kempener P.K. (IRENA), a. H. U. Of Colorado), Smart Grids and Renewables a Guide for Effective Deployment 2013, Tech. Rep. November, International Renewable Energy Agency, U.S.
4. Dixon R., Eckhart M., Hales D., Thompson G. Rapport mondial 2012 sur les énergies renouvelables 2012, Tech. rep., Renewable Energy Pollicy Network for the 21st Century 2012.
5. De Vos K., Driesen J. Balancing management mechanisms for intermittent power sources - A case study for wind power in Belgium. 2009 6th International Conference on the European Energy Market 2009, 1-6. IEEE. 10.1109/EEM.2009.5207190.
6. Goutard E. Renewable energy resources in energy management systems. 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) 2010, 1-6. IEEE. 10.1109/ISGTEUROPE.2010.5639003.
7. Bandyo I. Control and management of renewable generation - necessity of look ahead study. 2013 IEEE Innovative Smart Grid Technologies-asia (ISGT Asia) 2013, 1-5. IEEE. 10.1109/ISGT-Asia.2013.6698726.
8. Kélouwani S., Agbossou K. Nonlinear model identification of wind turbine with a neural network. IEEE Trans. Energy Convers. 2004, 19(3):607-612.
9. Cardenas A., Guzman C., Agbossou K. Development of a FPGA Based Real-Time Power Analysis and Control for Distributed Generation Interface. IEEE Trans. Power Syst. 2012, 27:1343-1353.
10. Guzman C., Cardenas A., Agbossou K. Load sharing strategy for autonomous AC microgrids based on FPGA implementation of ADALINE&FLL. IEEE Trans. Energy Convers. 2014, 29(3):663-672.
11. Hurst H.E. Long term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 1951, 116:509-516.
12. Soman S.S., Zareipour H., Malik O., Mandal P. A review of wind power and wind speed forecasting methods with different time horizons. North American Power Symposium 2010 2010, 1-8. IEEE. 10.1109/NAPS.2010.5619586.
13. 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. 10.1016/j.rser.2008.02.002.
14. Erdem E., Shi J. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 2011, 88(4):1405-1414. 10.1016/j.apenergy.2010.10.031.
15. Gao S., He Y., Chen H. Wind speed forecast for wind farms based on ARMA-ARCH model. 2009 International Conference on Sustainable Power Generation and Supply 2009, 1-4. IEEE. 10.1109/SUPERGEN.2009.5348142.
16. Bhaskar M., Jain A., Venkata Srinath N. Wind speed forecasting: Present status. 2010 International Conference on Power System Technology 2010, 1-6. 10.1109/POWERCON.2010.5666623.
17. Blonbou R. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning. Renew. Energy 2011, 36(3):1118-1124. 10.1016/j.renene.2010.08.026.
18. de Aquino R.R.B., Lira M.M.S., de Oliveira J.B., Carvalho M.A., Neto O.N., de Almeida G.J. Application of wavelet and neural network models for wind speed and power generation forecasting in a Brazilian experimental wind park. 2009 International Joint Conference on Neural Networks 2009, 172-178. IEEE. 10.1109/IJCNN.2009.5178791.
19. Catalao J.P.S., Pousinho H.M.I., Mendes V.M.F. Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal. IEEE Trans. Sustain. Energy 2010, 2(1):50-59. 10.1109/TSTE.2010.2076359.
20. Chen N., Qian Z., Nabney I.T., Meng X. Wind power forecasts using Gaussian processes and numerical weather prediction. IEEE Trans. Power Syst. 2014, 29(2):656-665. 10.1109/TPWRS.2013.2282366.
21. Pelikan E., Eben K., Resler J., Jurus P., Krc P., Brabec M., Brabec T., Musilek P. Wind power forecasting by an empirical model using NWP outputs. 2010 9th International Conference on Environment and Electrical Engineering 2010, 45-48. IEEE. 10.1109/EEEIC.2010.5490019.
22. Shi J., Ding Z., Lee W.-J., Yang Y., Liu Y., Zhang M. Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features. IEEE Trans. Smart Grid 2014, 5(1):521-526. 10.1109/TSG.2013.2283269.
23. Hu J., Wang J., Zeng G. A hybrid forecasting approach applied to wind speed time series. Renew. Energy 2013, 60:185-194. 10.1016/j.renene.2013.05.012.
24. Ben Taieb S., Sorjamaa A., Bontempi G. Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing 2010, 73(10-12):1950-1957. 10.1016/j.neucom.2009.11.030.
25. Herrera L., Pomares H., Rojas I., Guillén A., Prieto A., Valenzuela O. Recursive prediction for long term time series forecasting using advanced models. Neurocomputing 2007, 70(16-18):2870-2880. 10.1016/j.neucom.2006.04.015.
26. Bennani Y. Apprentissage par réseaux de neurones artificiels 2014, Tech. rep., Université de Paris 13, Ecole de Printemps sur l'Aprentissage arTificiel, Paris (.
27. Bhaskar K., Singh S.N. AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans. Sustain. Energy 2012, 3(2):306-315. 10.1109/TSTE.2011.2182215.
28. Pouliot M. La détermination des coefficients des ondelette de Daubechies 2009, Université de Laval, Ph.D. thesis.
29. Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. Pattern Anal. Mach. Intell. IEEE 1989, I(7):674-693.
30. Dubois S. Décomposition spatio-temporelles pour l'étude des textures dynamiques 2010, Université de la Rochelle, Ph.D. thesis.
31. Ho T. Étude de la méthode de la transformation en ondelette et application à la compression des images Remerciements 2005, Tech. rep., Rapport final de TIPE, Luaong Hông Viêt.
32. Unser M., Blu T. Wavelet theory demystified. IEEE Trans. Signal Process. 2003, 51(2):470-483. 10.1109/TSP.2002.807000.
33. Daubechies I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 1988, 41:909-996.
34. Khalighi S., Sousa T., Oliveira D., Pires G., Nunes U. Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM., Conference proceedings : annual international conference of the IEEE engineering in medicine and biology society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2011 2011, 3306-3309. 10.1109/IEMBS.2011.6090897.
35. Ahmad S. A Temporal Pattern Identification and Summarization Method for Complex Time Serial Data 2007, University of Surrey, Ph.D. thesis.
36. Gretillat J.-d. Modification de l'algorithme de la transformée en ondelettes descrète pour l'obtention d'une représentation invariante sous rotation 2006, Université Laval, Québec, Ph.D. thesis.
37. Oussar Y. Réseaux d'ondelettes et réseaux de neurones pour la modélisation statique et dynamique de processus 1998, VI. Université Paris, Ph.D. thesis.
38. Qian B., Rasheed K. Hurst exponent and financial market predictability. IASTED International Conference on Financial, 2004 2004, 1-7.
39. Delignières D. L'analyse des processus stochastiques, Sport, Performance, Santé", EA 2001, 1-12. doi:EA2991.
40. Sánchez Granero M., Trinidad Segovia J., García Pérez J. Some comments on Hurst exponent and the long memory processes on capital markets. Phys. A Stat. Mech. Appl. 2008, 387(22):5543-5551. 10.1016/j.physa.2008.05.053.
41. Eom C., Choi S., Oh G., Jung W.-S. Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets. Phys. A Stat. Mech. its Appl. 2008, 387(18):4630-4636. arXiv:arXiv:0712.1624v1. 10.1016/j.physa.2008.03.035.
42. Duan M. Time Series Predictability 2002, Marquette University, Milwaukee, Wisconsin, Ph.D. thesis.
43. Armstrong J., Collopy F. Error measures for generalizing about forecasting methods: empirical comparisons. Int. J. Forecast. 1992, 8(1):69-80. 10.1016/0169-2070(92)90008-W.
44. Shcherbakov M., Brebels A. A survey of forecast error measures. World Appl. Sci. J. 2013, 24(4):171-176. 10.5829/idosi.wasj.2013.24.itmies.80032.