Combined Prediction of Wind Power Using Multi-Dimension Embedding Phase Space

Qiang Wang; Yang Yang

doi:10.4028/www.scientific.net/AMM.321-324.838

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324Combined Prediction of Wind Power Using...

Combined Prediction of Wind Power Using Multi-Dimension Embedding Phase Space

Abstract:

In order to diminish the effect of reconstructed parameters to prediction of chaotic, a combined model for wind power prediction based on multi-dimension embedding is proposed. The combined model makes use of neural network method to achieve combination of several neural networks models based on phase space reconstruction, which can synthesize information and fuse prediction deviation in different embedding dimension, resulting in forecast accuracy improved. Simulation is performed to the real power time series Fujin wind farm. The results show that the combined prediction model is effective, and the prediction error of neural network combination is less than 7%.

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Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

838-841

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.838

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Online since:

June 2013

Authors:

Qiang Wang, Yang Yang

Keywords:

Embedding Dimension, Linear Weighted Combination, Neural Network Combination, Phase Space Reconstruction, Wind Power Prediction

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