论文信息 - Application of neural network to wind energy conversion systems

Application of neural network to wind energy conversion systems

In the previous study, the energy generation of two WECS deployed at Darling site were estimated using the statistical-based technique. A total energy generation of 178.43 MWh at 50 m and 196.17 MWh at 70 m height were estimated from both WECS for the month of January 2010. In this study, the energy generations of both WECS for the same month were estimated using a developed wind power predictor. The recorded five-minute averaged weather data for the period of 32 days (31st December 2009 to 31 January 2010) were obtained for this study. The long term power generations of both WECS in time steps of five-minute of up to one-week ahead are predicted for the month of January 2010. The forecast results are compared to the actual energy generations obtained from the statistical-based technique. The forecast model returns an absolute error value of 5.265%, mean absolute error value of 0.402%, and standard deviation of absolute error of 4.504% for the 1.3 MW WECS power prediction. For the 1.0 MW WECS, an absolute...

Zaccheus O. Olaofe | Zaccheus Olaofe Olaofe

[1] Andrew Kusiak,et al. Wind farm power prediction: a data‐mining approach , 2009 .

[2] Paras Mandal,et al. A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[3] Makarand Deo,et al. Real time wave forecasting using neural networks , 1998 .

[4] Ruddy Blonbou,et al. Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[5] R.M.R. Kainkwa. Wind speed pattern and the available wind power at Basotu, Tanzania , 2000 .

[6] A. Celik. A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[7] G. Strbac,et al. Trading Wind Generation in Short-Term Energy Markets , 2002, IEEE Power Engineering Review.

[8] Thomas Ackermann,et al. Wind Power in Power Systems , 2005 .

[9] Eric R. Scerri,et al. Wind data evaluation in the Maltese Islands , 1996 .

[10] K. A. Folly,et al. Wind power estimation using recurrent neural network technique , 2012, IEEE Power and Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources (PowerAfrica).