The Correlation Analysis of Clean Energy Output Based on Nonparametric Kernel Density Estimation Probability Models

摘要

There often exists correlation among adjacent photovoltaic power stations and wind farms. This paper provides a method based on the nonparametric kernel density estimation theory and Spearman rank correlation to analysis the correlation of clean energy outputs. Firstly, a variable bandwidth kernel density estimation method based on boundary kernel algorithm is built to establish the clean energy probability density model. The method can solve the boundary deviation and reflect the thick tail characteristics of clean energy, which improves the accuracy and practicability of the probability estimation. Next, some data from photovoltaic stations and wind farms of a certain power grid in Northwest China are used to obtain the probability density using above method, and simulated outputs of different wind farms and photovoltaic power stations. Then the complementary between clean energy's outputs are analyzed by using Spearman rank correlation coefficient. It can be concluded from the results that the probability models established by the nonparametric kernel density estimation theory can improve the accuracy and practicability of the probability estimations and the Spearman rank correlation coefficient can effectively reflect the output correlation of clean energy.