| Title |
Probabilistic Model for Wind Power Generation with Kernel Density Estimation |
| Authors |
최주승(Juseung Choi) ; 백승묵(Seung-Mook Baek) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.1.79 |
| Keywords |
Kernel Density Estimation; Power Curve; Weibull Distribution; Wind Power Distribution; Wind Speed Distribution |
| Abstract |
As the proportion of renewable energy sources that are intermittent and variability increases, system uncertainty is increasing, and a probabilistic analysis that can reflect these characteristics is required. In this paper, we propose a method for generating a probablistic wind power generation model using kernel density estimation. The boundary bias problem caused by using kernel density estimation for wind power generation data is eliminated through the boundary correction method, and a bandwidth suitable for the data can be selected through the bandwidth selector. Scenarios were created from probabilistic models made in various ways, and the accuracy of the models was validated by comparing them with real data. As a result, the accuracy was improved when the scenario was created by generating the model directly from the wind power generation data by estimating the kernel density estimation. |