A wind power probabilistic prediction method combines kernel density estimation and copula

The intermittent and fluctuating of new energy bring significant uncertainty to the power system, which greatly increases the risk of safe and stable operation of the power system. Compared with classical point prediction, probabilistic prediction of wind power can effectively quantify the uncertainty of its output, provide more comprehensive prediction information, and provide more reliable data support for power system analysis and decision-making. In this paper, this paper proposes a wind power probability prediction method that combines kernel density estimation and copula, first the kernel density estimation method is used to estimate the marginal probability distribution of univariate variables, and proposing a thumb rule and grid search method to optimize the bandwidth h; then based on Copula theory to estimate the joint distribution of multiple variables; finally, based on the conditional probability formula, calculate the probability density and confidence interval of wind power conditions under specified predicted wind power and predicted wind speed. The actual data of a wind farm station in Inner Mongolia is taken as an example to calculate, the result shows that compared with the conventional kernel density estimation method, the interval coverage rate of the proposed method is closer to the preset confidence level, and the average width of the interval is also narrower.