Paper Title:
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.

  Info
Periodical
Advanced Materials Research (Volumes 347-353)
Chapter
Chapter 9: Energy Chemical Engineering
Edited by
Weiguo Pan, Jianxing Ren and Yongguang Li
Pages
3551-3554
DOI
10.4028/www.scientific.net/AMR.347-353.3551
Citation
X. L. Wang, Q. C. Chen, "Combined Forecasting for Short-Term Output Power of Wind Farm", Advanced Materials Research, Vols. 347-353, pp. 3551-3554, 2012
Online since
October 2011
Export
Price
$32.00
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