Short-term wind power prediction based onprincipal component analysis and genetic neural network

Short-term wind power prediction is important to the operation of power system with comparatively large amount of wind power, a short-circiut wind power predicting model based on principal component analysis (PCA) method and genetic neural network is proposed. PCA is applied to process original input data, the principal components are used as input data for neural network. In order to solve the problems of slow convergence speed and being easy to fall into local minimum of BP neural network, genetic algorithm(GA) is used to make a thorough searching for the initial weights and thresholds, and the Levenberg-Marquardt (L-M) method is used to finely train the network. Based on the actual data of a wind farm, the forecasting results by the proposed method is more precise than those by GA neural network model and PCA-LM neural network model, providing an effective way to forecast short-term wind power.