Wind Power Prediction Based on Kalman Filter and Non-parametric Kernel Density Estimation

Wind power point prediction can only give the exact predicted power value at a certain time point, and the fluctuation range of the predicted value cannot be obtained, which brings a lot of problems and troubles to wind power grid connection. In this paper, a short-term wind power interval prediction method based on Kalman filter and non-parametric kernel density estimation is proposed. First, the predicted value of wind power points is obtained by Kalman filter method; Second, the prediction error is statistically analyzed, and the probability density function of the error is obtained by kernel density estimation method; Finally, the fluctuation interval of wind power is predicted under the given confidence degree. Simulation analysis on the data of a wind farm in Oklahoma show at the proposed interval prediction method not only has higher accuracy but also has better general applicability.