A Survey of Wind Power Ramp Forecasting

DOI: 10.4236/epe.2013.54B071   PDF   HTML     5,444 Downloads   7,492 Views   Citations


At home and broad, more wind power is being installed in electricity markets, the influence brought by wind power become more important on power system stability, especially the fluctuation, the uncertainty in wind power production and multi-time scale of the wind. In order to forecast ramp events before the power system encountering failure, so that the operator can adopt some limited controlling strategy. This paper introduces the present status of the wind power ramp prediction at home and abroad. And it gives out four kinds of definitions of ramp events, which are used by many scholars, then provides various forecasting error algorithm. In the aspect of prediction models, it comes up with physical models and statistical models, and enumerates various examples of different models. Finally, it prospects the tendency of the model improvement about the wind power ramp events forecasting, which would be significant for ramp research.

Share and Cite:

T. Ouyang, X. Zha and L. Qin, "A Survey of Wind Power Ramp Forecasting," Energy and Power Engineering, Vol. 5 No. 4B, 2013, pp. 368-372. doi: 10.4236/epe.2013.54B071.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] S. Y. Kim and S. H. Kim, “Comparative Study on the Performance of Various Wind Speed Predictors,” KIISS, April 2011, pp. 21-24.
[2] Y. H. Mi, et al., “Prediction of Wind Power Generation and Power Ramp Rate with Time Series Analysis,” Awareness Science and Technology (iCAST), 2011 3rd International Conference on, 2011.
[3] C. Kamath, “Understanding Wind Ramp Events through Analysis of Historical Data,” IEEE PES Transmission and Distribution Conference and Exposition: Smart Solutions for a Changing World, 2010,
[4] C. Nicholas and Merlinde, et al., “Detecting, Categorizing and Article Forecasting Large Ramps in Wind Farm Power Output Using Meteorological Observations and WPPT,” Wind Energy, Vol. 10, No. 5, 2007, pp. 453-470. doi:10.1002/we.235
[5] J. F. Li, P. F. Shi and H. Gao, “The Report of Chinese Wind Power Development 2010,” Vol. 10, 2010.
[6] C. Ferreira, J. Gama and L. Matias, “A Survey on Wind Power Ramp Forecasting,” Argonne National Laboratory report, 2011.
[7] A. Sfetsos, “A Novel Approach for the Forecasting of the Mean Hourly Wind Speed Time Series,” Renewable Energy, Vol. 27, No. 2, 2002, pp. 163-174. doi:10.1016/S0960-1481(01)00193-8
[8] A. J. Smola and B. Schoelkopf, “A Tutorial on Support Vector Regression,” Statistics and Computing, Vol. 14, No. 3, 2004, pp. 199-222. doi:10.1023/B:STCO.0000035301.49549.88
[9] A. J. Svoboda, C. Tseng, C. Li and R. B. Johnson, “Short-term Resource Scheduling with Ramp Constraints,” IEEE Transactions on Power Systems, Vol. 12, No. 1, 1997, pp. 77-83. doi:10.1109/59.574926
[10] L. A. Landberg, “Mathematical Look at a Physical Power Prediction Model,” Wind Energy, Vol. 1, 1998, pp. 23-28.
[11] L. Landberg, L. Myllerup, O. Rathmann, E. Lundtang Petersen, B. Hoffmann Jorgensen, J. Badger and N. Gylling, “Mortensen Wind Resource Estimation – An Overview,” Wind Energy, Vol. 6, No. 3, 2003, pp. 26-71.
[12] M. Magnusson and L. Wern, “Wind Energy Predictions Using CFD and HIRLAM Forecast,” Proceedings of the European Wind Energy Conference EWEC2001, Copenhagen, Denmark, 2001.
[13] HR Glahn, DA Lowry, “The Use of Model Output Statistics (MOS) in Objective Weather Forecasting,” Journal of Applied Meteorology, Vol. 11, No. 8, 1972.
[14] J. L. Torres, A. García, M. de Blas and A. de Francisco, “Forecast of Hourly Averages Wind Speed with ARMA Models in Navarre,” Solar Energy, Vol. 79, No. 1, 2005, pp. 65-77.
[15] R. L. Welch, S. M. Ruffing and G. K. Venayagamoorthy, “Comparison of Feed Forward and Feedback Neural Network Architectures for Short-term Wind Speed Prediction,” Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, 2009.
[16] T. H. M. El-Fouly, E. F. El-Saadany and M. M. A. Salama, “Grey predictor for Wind Energy Conversion Systems Output Power Prediction,” IEEE Transactions on Power System, Vol. 21, 2006.
[17] I. G. Damousis and P. Dokopoulos, “A Fuzzy Model Expert System for the Forecasting of Wind Speed and Power Generation in Wind Farms,” Proceedings of the IEEE International Conference on Power Industry Computer Applications PICA 01, 2001.
[18] H. Mori and Y. Umezawa, “Application of NB Tree to Selection of Meteorological Variables in Wind Speed Prediction,” Proceedings of the IEEE Transmission & Distribution Conference & Exposition, Asia and Pacific; 2009.
[19] R. Jursa, “Wind Power Prediction with Different Artificial Intelligence Models,” Proceedings of the European Wind Energy Conference, EWEC2007, Milan, Italy, 2007.
[20] M. A. Mohandes, T. O. Halawani, S. Rehman and A. A. Hussain, “Support Vector Machines for Wind Speed Prediction,” Renewable Energy, Vol. 29, No. 6, 2004.
[21] M. Negnevitsky, P. Johnson and S. Santoso, “Short-term Wind Power Forecasting Using Hybrid Intelligent Systems,” Proceedings of the IEEE Power Engineering Society General Meeting, Tampa, Florida, USA, 2007.
[22] Sevlian, Raffi, Rajagopal and Ram, “Wind Power Ramps: Detection and Statistics,” IEEE Power and Energy Society General Meeting, 2012.
[23] M. C. Alexiadis, P. S. Dokopoulos, and H. S. Sahsamanoglou, “Wind Speed and Power Forecasting Based on Spatial Correlation Models,” IEEE Transaction on Energy Conversion, Vol. 14, No. 3, 1999, pp. 836-842. doi:10.1109/60.790962
[24] C. W. Potter and M. Negnevitsky, “Very Short-term Wind Forecasting for Tasmanian Power Generation,” IEEE Transaction on Power System, Vol. 21, No. 2, 2006, pp. 965-972. doi:10.1109/TPWRS.2006.873421
[25] Kusiak, H. Zheng and Z. Song, “Short-term Prediction of Wind Farm Power: A Data Mining Approach,” IEEE Trans. Energy Conversion, Vol. 24, No. 1, 2009, pp. 125-136. doi:10.1109/TEC.2008.2006552
[26] P. Pinson and G. Kariniotakis, “On-line Assessment of Prediction Risk for Wind Power Production Forecasts,” in Proceedings of the European Wind Energy Conference and Exhibition, 2003.
[27] H. Zareipour, H. Dongliang and W. Rosehart, “Wind Power Ramp Events Classification and Forecasting: A Data Mining Approach,” Power and Energy Society General Meeting, Detroit, USA, 2011.
[28] H. Hamilton, “A New Approach to the Economic Analysis of Nonstationay Time-series and Business Cycles,” Econometrica, Vol. 57, No. 2, 1989, pp. 357-384. doi:10.2307/1912559
[29] A. W. Robertson, S. Kirshner and P. Smyth, “Hidden Markov Models for Modeling Daily Rainfall Occurence over Brazil,” Report UCI-ICS-03-27, Information and Computer Sciences, University of California, Irvine (California) 2003.
[30] P. Ailliot and V. Monbet, “Markov Switching Autoregressive Models for Wind Time Series,” Journal of Statistical Planning and Inference (submitted) 2006.
[31] P. Pinson, L. E. A. Christensen, H. Madsen, P. E. Sorensen, M. H. Donovan and L. E. Jensen, “Regime-switching Modeling of the Fluctuations of Offshore wind Generation,” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 96 No.12, 2008, pp. 2327-2347. doi:10.1016/j.jweia.2008.03.010

comments powered by Disqus

Copyright © 2020 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.