Wind Power Combination Forecasting Model Based on Drift

Abstract:

As different wind power forecasting methods provide different information and differ in forecast precision, the combined wind power forecasting model is employed to better forecast wind power. Wind power combination forecasting model based on drift and complementarity of different single wind power forecasting models is proposed in this paper. The combination forecasting model provides a new solution to wind power forecast. Finally a practical example is given to show that wind power combination forecasting model based on drift can improve forecasting precision and is effective in practice.

• Periodical: Advanced Materials Research (Volumes 953-954)
• Pages: 522-528
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.953-954.522
• Online since: June 2014
• Authors: Quan Cheng Lyu*, Wen Ying Liu, Dan Dan Zhu, Wei Zhou Wang, Xu Shan Han, Fu Chao Liu
• Keywords: Combination, Compatibility, Complementary, Drift, Forecasting, Wind Power

References

[1] Chi Yongning, Liu Yanhua, Wang Weisheng, et al．Study on impact of wind power integration on power system [J]．Power System Technology, 2007 ，31(3) ：77 - 81( in Chinese).

[2] Yang X, Xiao Y, Chen S. Wind speed and generated power forecasting in wind farm[J]. Proceeding-Chinese Society of Electrical Engineering, 2005, 25(11): 1.

[3] Bilgili M, Sahin B, Yasar A. Application of artificial neural networks for the wind speed prediction of target station using reference stations data[J]. Renewable Energy, 2007, 32(14): 2350-2360.

DOI: 10.1016/j.renene.2006.12.001

[4] Ding M, Zhang L, Wu Y. Wind speed forecast model for wind farms based on time series analysis [J]. Electric Power Automation Equipment, 2005, 8: 32-34.

[5] Bossanyi E A. Short-term wind prediction using Kalman filters[J]. Wind Engineering, 1985, 9(1): 1-8.

[6] Sun C, Wang Y, Li X. A vector autoregression model of hourly wind speed and its applications in hourly wind speed forecasting[J]. Proceeding-Chinese Society of Electrical Engineering, 2008, 28(14): 112.

[7] Liu C, Fang, Wang W, et al. A Combination Forecasting Model for Wind Farm Output Power [J][J]. Power System Technology, 2009, 13: 017.

[8] Wang L J, Dong L, Hu G F, et al. Combined prediction of wind power generation in multi-dimension embedding phase space[J]. Control and Decision, 2010, 25(4).

[9] Meijuan Li, Guohong Chen, Zhibing Lin. A Combination Forecasting Model Based on Drift[J]. China Management Science, 2011, 19(3): 111-117.