Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

Abstract:

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

• Periodical: Applied Mechanics and Materials (Volume 733)
• Pages: 893-897
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.733.893
• Online since: February 2015
• Authors: Peng Yu Zhang*
• Keywords: Generalized Regression Neural Network, Nonlinear Combination Forecasting, PSO Algorithm, Short-Term Wind Power Prediction

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