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

DOI

10.2991/aiie-16.2016.6How to use a DOI?

Keywords

Nonparametric kernel density estimation; variable bandwidth; clean energy; Spearman rank correlation coefficient

Abstract

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaolin Qin
AU  - Yonggang Li
AU  - Chao Shen
AU  - Zengqiang Zhang
AU  - Xuyao Zeng
PY  - 2016/11
DA  - 2016/11
TI  - The Correlation Analysis of Clean Energy Output Based on Nonparametric Kernel Density Estimation Probability Models
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 24
EP  - 28
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.6
DO  - 10.2991/aiie-16.2016.6
ID  - Qin2016/11
ER  -