Wavelet Neural Network Model for Short-Term Wind Power Forecasting Based on Particle Swarm Optimization Algorithm

Abstract:

The accuracy of short-term wind power forecast is important to the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network is proposed. At the same time in order to overcome the disadvantages of the wavelet neural network for only use error reverse transmission as a fixed rule, this paper puts forward using Particle Swarm Optimization algorithm to replace the traditional gradient descent method training wavelet neural network. Through the analysis of the measured data of a wind farm, Shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 806-812
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.806
• Online since: March 2014
• Authors: Ye Chen*
• Keywords: Particle Swarm Optimization Algorithm, Prediction Error, Short-Term Wind Power Prediction, Wavelet Neural Network

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