A Literature Review of Wind Forecasting Methods

Abstract

In this paper, an overview of new and current developments in wind forecasting is given where the focus lies upon principles and practical implementations. High penetration of wind power in the electricity system provides many challenges to the power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help the power system operators reduce the risk of unreliability of electricity supply. This paper gives a literature survey on the categories and major methods of wind forecasting. Based on the assessment of wind speed and power forecasting methods, the future development direction of wind forecasting is proposed.

Share and Cite:

Chang, W. (2014) A Literature Review of Wind Forecasting Methods. Journal of Power and Energy Engineering, 2, 161-168. doi: 10.4236/jpee.2014.24023.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Chang, W.Y. (2013) Short-Term Wind Power Forecasting Using EPSO Based Hybrid Method. Energies, 6, 4879-4896. http://dx.doi.org/10.3390/en6094879
[2] Chang, W.Y. (2013) Comparison of Three Short Term Wind Power Forecasting Systems. Advanced Materials Research, 684, 671-675. http://dx.doi.org/10.4028/www.scientific.net/AMR.684.671
[3] Chang, W.Y. (2013) An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting. Journal of Applied Mathematics, 2013, Article ID: 971389, 9 p.
[4] Sideratos, G. and Hatziargyriou, N.D. (2007) An Advanced Statistical Method for Wind Power Forecasting. IEEE Transactions on Power Systems, 22, 258-265. http://dx.doi.org/10.1109/TPWRS.2006.889078
[5] Ma, L., Luan, S.Y., Jiang, C.W., Liu, H L. and Zhang, Y. (2009) A Review on the Forecasting of Wind Speed and Generated Power. Renewable and Sustainable Energy Reviews, 13, 915-920. http://dx.doi.org/10.1016/j.rser.2008.02.002
[6] Lange, M. and Focken, U. (2008) New Developments in Wind Energy Forecasting. Proceedings of the 2008 IEEE Power and Energy Society General Meeting, Pittsburgh, 20-24 July 2008, 1-8.
[7] Wang, X.C., Guo, P. and Huang, X.B. (2011) A Review of Wind Power Forecasting Models. Energy Procedia, 12, 770-778. http://dx.doi.org/10.1016/j.egypro.2011.10.103
[8] Zhao, D.M., Zhu, Y.C. and Zhang, X. (2011) Research on Wind Power Forecasting in Wind Farms. Proceedings of the 2011 IEEE Power Engineering and Automation Conference, Wuhan, 8-9 September 2011, 175-178. http://dx.doi.org/10.1109/PEAM.2011.6134829
[9] Zhao, X., Wang, S.X. and Li, T. (2011) Review of Evaluation Criteria and Main Methods of Wind Power Forecasting. Energy Procedia, 12, 761-769. http://dx.doi.org/10.1016/j.egypro.2011.10.102
[10] Wu, Y.K. and Hon, J.S. (2007) A Literature Review of Wind Forecasting Technology in the World. Proceedings of the IEEE Conference on Power Tech, Lausanne, 1-5 July 2007, 504-509.
[11] Soman, S.S., Zareipour, H., Malik, O. and Mandal, P. (2010) A Review of Wind Power and Wind Speed Forecasting Methods with Different Time Horizons. Proceedings of the 2010 North American Power Symposium, Arlington, 26-28 September 2010, 1-8. http://dx.doi.org/10.1109/NAPS.2010.5619586
[12] Bhaskar, K. and Singh, S.N. (2012) AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network. IEEE Transactions on Sustainable Energy, 3, 306-315. http://dx.doi.org/10.1109/TSTE.2011.2182215
[13] Firat, U., Engin, S.N., Saraclar, M. and Ertuzun, A.B. (2010) Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model. Proceedings of the 9th International Conference on Machine Learning and Applications, Washington, 12-14 December 2010, 618-621.
[14] Erdem, E. and Shi, J. (2011) ARMA Based Approaches for Forecasting the Tuple of Wind Speed and Direction. Applied Energy, 88, 1405-1414. http://dx.doi.org/10.1016/j.apenergy.2010.10.031
[15] Li, L.L., Li, J.H., He, P.J. and Wang, C.S. (2011) The Use of Wavelet Theory and ARMA Model in Wind Speed Prediction. Proceedings of the 1st International Conference on Electric Power Equipment-Switching Technology, Xi’an, 23-27 October 2011, 395-398.
[16] Palomares-Salas, J.C., de la Rosa, J.J.G., Ramiro, J.G., Melgar, J., Aguera, A. and Moreno, A. (2009) ARIMA vs. Neural Networks for Wind Speed Forecasting. Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Hong Kong, 11-13 May 2009, 129-133.
[17] Miranda, M.S. and Dunn, R.W. (2006) One-Hour-Ahead Wind Speed Prediction Using a Bayesian Methodology. Proceedings of the 2006 IEEE Power Engineering Society General Meeting, Montreal, 18-22 June 2006, 1-6.
[18] Alexiadis, M.C., Dokopoulos, P.S., Sahsamanoglou, H.S. and Manousaridis, I.M. (1998) Short-Term Forecasting of Wind Speed and Related Electrical Power. Solar Energy, 63, 61-68. http://dx.doi.org/10.1016/S0038-092X(98)00032-2
[19] Alexiadis, M.C., Dokopoulos, P.S. and Sahsamanoglou, H.S. (1999) Wind Speed and Power Forecasting Based on Spatial Correlation Models. IEEE Transactions on Energy Conversion, 14, 836-842. http://dx.doi.org/10.1109/60.790962
[20] Barbounis, T.G. and Theocharis, J.B. (2007) A Locally Recurrent Fuzzy Neural Network with Application to the Wind Speed Prediction Using Spatial Correlation. Neurocomputing, 70, 1525-1542. http://dx.doi.org/10.1016/j.neucom.2006.01.032
[21] Wu, Y.K., Lee, C.Y., Tsai, S.H. and Yu, S.N. (2010) Actual Experience on the Short-Term Wind Power Forecasting at Penghu-From an Island Perspective. Proceedings of the 2010 International Conference on Power System Technology, Hangzhou, 24-28 October 2010, 1-8. http://dx.doi.org/10.1109/POWERCON.2010.5666092
[22] Sfetsos, A. (2002) A Novel Approach for the Forecasting of Mean Hourly Wind Speed Time Series. Renewable Energy, 27, 163-174. http://dx.doi.org/10.1016/S0960-1481(01)00193-8
[23] Chang, W.Y. (2013) Application of Back Propagation Neural Network for Wind Power Generation Forecasting. International Journal of Digital Content Technology and its Application, 7, 502-509.
[24] More, A. and Deo, M.C. (2003) Forecasting Wind with Neural Networks. Marine Structures, 16, 35-49. http://dx.doi.org/10.1016/S0951-8339(02)00053-9
[25] Chang, W.Y. (2013) Wind Energy Conversion System Power Forecasting Using Radial Basis Function Neural Network. Applied Mechanics and Materials, 284-287, 1067-1071. http://dx.doi.org/10.4028/www.scientific.net/AMM.284-287.1067
[26] Guo, Z.H., Zhao, W.G., Lu, H.Y. and Wang, J.Z. (2012) Multi-Step Forecasting for Wind Speed Using a Modified EMD-Based Artificial Neural Network Model. Renewable Energy, 37, 241-249. http://dx.doi.org/10.1016/j.renene.2011.06.023
[27] Li, G. and Shi, J. (2010) On Comparing Three Artificial Nneural Networks for Wind Speed Forecasting. Applied Energy, 87, 2313-2320. http://dx.doi.org/10.1016/j.apenergy.2009.12.013
[28] Yang, Z.L., Liu, Y.Q. and Li, C.R. (2011) Interpolation of Missing Wind Data Based on ANFIS. Renewable Energy, 36, 993-998. http://dx.doi.org/10.1016/j.renene.2010.08.033
[29] Zeng, J.W. and Qiao, W. (2011) Support Vector Machine-Based Short-Term Wind Power Forecasting. Proceedings of the IEEE/PES Power Systems Conference and Exposition, Phoenix, 20-23 March 2011, 1-8.
[30] Zhou, J.Y., Shi, J. and Li, G. (2011) Fine Tuning Support Vector Machines for Short-Term Wind Speed Forecasting. Energy Conversion and Management, 52, 1990-1998. http://dx.doi.org/10.1016/j.enconman.2010.11.007
[31] Xia, J.R., Zhao, P. and Dai, Y.P. (2010) Neuro-Fuzzy Networks for Short-Term Wind Power Forecasting. Proceedings of the International Conference on Power System Technology, Hangzhou, 24-28 October 2010, 1-5. http://dx.doi.org/10.1115/1.859612
[32] Jursa, R. and Rohrig, K. (2008) Short-Term Wind Power Forecasting Using Evolutionary Algorithms for the Automated Specification of Artificial Intelligence Models. International Journal of Forecasting, 24, 694-709. http://dx.doi.org/10.1016/j.ijforecast.2008.08.007
[33] Zhao, P., Wang, J.F., Xia, J.R., Dai, Y.P., Sheng, Y.X. and Yue, J. (2012) Performance Evaluation and Accuracy Enhancement of a Day-Ahead Wind Power Forecasting System in China. Renewable Energy, 43, 234-241. http://dx.doi.org/10.1016/j.renene.2011.11.051
[34] Shi, J., Guo, J.M. and Zheng, S.T. (2012) Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series. Renewable and Sustainable Energy Reviews, 16, 3471-3480. http://dx.doi.org/10.1016/j.rser.2012.02.044
[35] Guo, Z.H., Wu, J., Lu, H.Y. and Wang, J.Z. (2011) A Case Study on a Hybrid Wind Speed Forecasting Method Using BP Neural Network. Knowledge-Based Systems, 24, 1048-1056. http://dx.doi.org/10.1016/j.knosys.2011.04.019
[36] Catalão, J.P.S., Pousinho, H.M.I. and Mendes, V.M.F. (2011) Short-Term Wind Power Forecasting in Portugal by Neural Networks and Wavelet Transform. Renewable Energy, 36, 1245-1251. http://dx.doi.org/10.1016/j.renene.2010.09.016
[37] Ernst, B., Oakleaf, B., Ahlstrom, M.L., Lange, M., Moehrlen, C., Lange, B., Focken, U. and Rohrig, K. (2007) Predicting the Wind. IEEE Power and Energy Magazine, 5, 78-89. http://dx.doi.org/10.1109/MPE.2007.906306
[38] Foley, A.M., Leahy, P.G., Marvuglia, A. and McKeogh, E.J. (2012) Current Methods and Advances in Forecasting of Wind Power Generation. Renewable Energy, 37, 1-8. http://dx.doi.org/10.1016/j.renene.2011.05.033

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.