The Combination Model Of Wind Power Prediction

The main purpose of this paper is combining the models of the BP neural network, the wavelet neural network and the support vector machine (SVM) for wind power forecast, in order to improve the prediction precision of wind power.The first chapter is about the introduction. Simply introduce the development of the industry of wind power and the significance of wind power prediction.The second chapter is about the theory and application of the BP neural network and tight wavelet neural network. The BP neural network can be fully approximate any complicated nonlinear relation, and the parallel distributed processing method can realize a large amount of computation, can also learn and adaptive unknown system, which determines that the neural network can be used for wind power prediction research.Wavelet neural network is a feed-forward network based on the theory of wavelet, which advantage is the determined structure and a strong learning ability, can avoid to the blindness of topological structure design, and thoroughly avoid to the local optimization problem.The third chapter is about the support vector machine. SVM is based on the statistical learning theory, reflects the structural risk minimization. It can change predict task to the standard quadratic programming forms, which means to a tight convex set. It ensures that the SVM can converge to the global optimal solution, Because a quadratic programming problem can be quickly calculated, SVM is applied to predict areas quickly.The fourth chapter is about the combination model. The combination is based on the first two chapters of the model, using covariance optimal combination forecast method, and increase the prediction accuracy.