Research On Wind Power Prediction And Optimization Algorithm Based On Neural Network

Wind power generation is one of the main forms of people using non-polluting and renewable wind energy. Due to the random nature of wind power, the wind form plant that wants to connect to the grid has been limited, that is, it must have a high accuracy of short-term wind power forecasting system. In our country, the research on this aspect starts late, and the accuracy of the forecasting model can't match the application requirements. In this paper, the short-term power forecasting model of the wind farm is studied. The historical numerical weather forecast data of the single and the multi position, a variety of intelligent optimization algorithm and the prediction model are combined. On the Matlab simulation platform the study and the experimental verification are carried out.This paper summarizes the prediction method of BP neural network and support vector machine for wind power, analyzes the advantages and disadvantages of the two methods, and established a BP neural network model for short-term wind power prediction based on numerical weather prediction, simulation is realized in Matlab; the research of support vector machine for wind power prediction model parameter optimization problem. The use of particle swarm algorithm, particle swarm optimization algorithm of support vector machine penalty factor and kernel function parameters of RBF, an improved particle swarm optimization algorithm based on support vector machine model, and realizes the contrast effect of algorithm verification and analysis in the simulation experiment; using genetic algorithm to optimize the prediction of initial weights of BP neural threshold model network, a BP neural network model was established based on genetic algorithm optimization,using principal component analysis to reduce the dimension of the wind field of different position and height of the historical weather data, forecast the influence of data on wind power, as the training data of BP neural network and BP neural network model after optimization. To achieve the efficient use of multidimensional input data.Finally, the technical points of wind power prediction system are analyzed based on SPWF-3000 wind power prediction system.