Wind Power Forecasting Based on the BP Neural Network

Abstract:

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.

• Periodical: Applied Mechanics and Materials (Volumes 341-342)
• Pages: 1303-1307
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.341-342.1303
• Online since: July 2013
• Authors: Jian Dong Mao, Xiao Jing Zhang, Juan Li
• Keywords: BP Neural Network (BPNN), Short-Term Forecast, Wind Power

References

[1] Xingkai GU, Gaofeng FAN, Xiaorong WANG, Haixiang ZHAO, Huizhu DAI. Summarization of Wind Power Prediction Technology. Power System Technology, 2007, (31) S2: 335-338.

[2] Xiaomei WU, Yinming BAI, Fushuan WEN. Short-term wind power forecast based on the Radial Basis Function neural network. Power System Protection and Control, 2011, (39) 15: 80-83.

[3] Brown B. G., Katz R. W., Murphy A. H. Time series models to simulate and forecast wind speed and wind power. Journal of Climate and Applied Meteorology, 1984(23): 1184-1195.

DOI: 10.1175/1520-0450(1984)023<1184:tsmtsa>2.0.co;2

[4] Andrew Boone. Simulation of short-term wind speed forecast errors using a multi-variate ARMA(1, 1) time-series model. Stockholm, Sweden: Royal Institute of Technology, (2005).

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

[6] Lexiadis M.A., Dokopoulos P., Samanoglou H. S., et al. Short term forecasting of wind speed and related electrical power. Solar Energy. 1998, 63(1): 61-68.

DOI: 10.1016/s0038-092x(98)00032-2

[7] Kariniotakis G. N., Stavrakakis G. S. Nogaret E. F. Wind power forecasting using advanced neural networks models. IEEE Transactions on Energy Conversion, 1996, 11(4): 762-767.

DOI: 10.1109/60.556376

[8] Gaofeng FAN, Weisheng WANG, Chun LIU, Huizhu DAI. Wind Power Prediction Based on Artificial Neural Network. Chin. Soc. for Elec. Eng., 2008, (28) 34: 118-123.

[9] Huang GUO, Haiyan ZHAO, Xiaofeng ZHANG, Jiansheng GUO, XiaoBin HUI. Model Selection Based on BP Neural Network. Journal of Projectiles Rockets Missiles and Guidance, 2007, (27) 5: 214-216.

[10] Chun Liu, Zheyi PEI, et al. Function criteria of wind power forecasting system. Dispatching and Communication Center of State Grid Corporation of China.