A Two-Stage Combination Model for Wind Power Forecasting

Jun Zhang; Yuan Hao Wang; Ying Yi Li; Feng Guo

doi:10.4028/www.scientific.net/AMM.737.9

Paper Titles

Preface and Conference Organization

The Analysis of Evolution Characteristic for Maximum Wind Speed in the Hexi Corridor
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A Two-Stage Combination Model for Wind Power Forecasting
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Analysis of Corn Straw Pyrolysis Bio-Oil Composition
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Influence of Solar Panels in Distributed Photovoltaic Power Generating System above Farm Land on Field and Crops
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 737A Two-Stage Combination Model for Wind Power...

A Two-Stage Combination Model for Wind Power Forecasting

Abstract:

With the wind farm data from the southeast coast this paper builds a two-stage combination forecasting model of output power based on data preprocessing which include filling up missing data and pre-decomposition. The first stage is a composite prediction of decomposed power sequence in which a time series and optimized BP neural network predict the general trend and the correlation of various factors respectively. The second stage is BP neural network with its input is the results of first stage. The effectiveness and accuracy of the two-stage combination model are verified by comparing the mean square error of the combination model and other models.

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Periodical:

Applied Mechanics and Materials (Volume 737)

Pages:

9-13

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.737.9

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Online since:

March 2015

Authors:

Jun Zhang, Yuan Hao Wang*, Ying Yi Li, Feng Guo

Keywords:

Combination Model, Neural Network, Wind Power Forecasting

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* - Corresponding Author

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