Combined Forecasting for Short-Term Output Power of Wind Farm

Xiao Lan Wang; Qian Cheng Chen

doi:10.4028/www.scientific.net/AMR.347-353.3551

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 347-353Combined Forecasting for Short-Term Output Power...

Combined Forecasting for Short-Term Output Power of Wind Farm

Abstract:

Wind power forecast is of great significance for power grid operation and scheduling. The effection of historical time series of output power and weather factors to wind power are considered in this paper. By use of BP neural network, an iterative forecasting model about output power time series is built. An Elman neural network forecasting model is established between numerical weather prediction data and output power. Then combining the above two forecasting models using covariance optimal combination method, a combined forecasting model for wind power is achieved so as to use all effective information of different data. The simulation experiment shows that the prediction accuracy has been improved by the combination forecast.

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Periodical:

Advanced Materials Research (Volumes 347-353)

Pages:

3551-3554

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.347-353.3551

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Online since:

October 2011

Authors:

Xiao Lan Wang, Qian Cheng Chen

Keywords:

BP Neural Network (BPNN), Combined Forecasting, Covariance Optimal, Elman Neural Network (ENN), Output Power, Short-Term

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