Study on Short-Term Wind Power Prediction Model Based on ARMA Theory

Wei Li; Hong Tu Zhang; Ting Ting An

doi:10.4028/www.scientific.net/AMM.448-453.1875

Paper Titles

Short-Term Wind Power Prediction and Error Analysis
p.1851

Performance Analysis of Coastal Wind Farms in Jiangsu, China
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Analysis of Low Voltage Ride-Through Capability for Variable Speed Wind Turbines Using Experimental and Measured Data
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Offshore Wind Turbines Operation and Maintenance in China: A Case Study of Donghai Bridge Offshore Wind Farm
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Study on Short-Term Wind Power Prediction Model Based on ARMA Theory
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Individual Pitch Control for Load Reduction Based on Norm Theory
p.1879

Non-Linear Control System for Wind Power Plant with DFIG
p.1884

Numerical Simulation on the Dynamic Stall of the Horizontal-Axis Wind Turbine
p.1888

Rotor Design and Performance Study of a Vertical-Axis Wind Turbine Based on DMS
p.1892

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 448-453Study on Short-Term Wind Power Prediction Model...

Study on Short-Term Wind Power Prediction Model Based on ARMA Theory

Abstract:

At present, the difficulty of wind power integration has resulted in a large number of wind curtailment phenomena and wasted a lot of renewable energy. Due to the significant instability, anti-peak-regulation and intermittency of wind power, wind power integration needs an accurate prediction technique to be a basis. ARMA model has the advantage of high prediction accuracy in predicting short-term wind power. This paper puts forward the method for short-term wind power prediction using ARMA model and carries out empirical analysis using the data from a wind farm of Jilin province, which shows the science and operability of the proposed model. It provides a new research method for the wind power prediction.

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Periodical:

Applied Mechanics and Materials (Volumes 448-453)

Pages:

1875-1878

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.448-453.1875

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Online since:

October 2013

Authors:

Wei Li, Hong Tu Zhang*, Ting Ting An

Keywords:

ARMA Model, Short-Term Prediction, Wind Power

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* - Corresponding Author

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