The Short-Term Wind Power Prediction Based on the Neural Network of Logistic Mapping Phase Space Reconstruction

摘要

It is difficult to be accurately predicted for wind power generation's random, intermittent and volatility. According to the strong chaotic characteristics of wind speed, the optimal time delay and embedding dimensions of wind speed are determined by using a short-term prediction of phase space reconstruction theory. After the sample space is reconstructed, the short-term wind speed is carried out by BP neural network. The experimental results show that the higher forecasting accuracy of short-term power generation can be obtained.