Research on modeling method describing wind power fluctuation probability density based on nonparametric kernel density estimation

2016 ◽  
Author(s):  
Daojun Chen ◽  
Hu Guo ◽  
Jian Zuo ◽  
Xuan Wang ◽  
Lei Zhang
Keyword(s):  
Probability Density ◽  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Modeling Method ◽  
Power Fluctuation ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel

Application of Nonparametric Kernel Density Estimation in Hongkong Stock Market

2011 ◽  
Vol 55-57 ◽  
pp. 209-214
Author(s):  
Yu Ling Wang ◽  
Jing Wang
Keyword(s):  
Distribution Function ◽  
Stock Returns ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
New Method ◽  
Fat Tails ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel ◽  
Better Than

A new method, non-parametric kernel density, is used to research the distribution function of HangSeng index returns. The new method can not only depict the character of peak and fat tails of stock returns, but also capture the market risk better than normal distribution. Further more, more accurate conclusions are concluded.

scholarly journals A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation

2005 ◽  
Vol 18 (1) ◽  
pp. 127-144 ◽  
Author(s):  
Codrut Ianasi ◽  
Vasile Gui ◽  
Corneliu Toma ◽  
Dan Pescaru
Keyword(s):  
Real Time ◽  
Density Estimation ◽  
Video Surveillance ◽  
Kernel Density Estimation ◽  
Computing Time ◽  
Kernel Density ◽  
Surveillance Systems ◽  
Event Analysis ◽  
Nonparametric Kernel Density ◽  
Nonparametric Kernel

Moving object detection and tracking in video surveillance systems is commonly based on background estimation and subtraction. For satisfactory performance in real world applications, robust estimators, tolerating the presence of outliers in the data, are needed. Nonparametric kernel density estimation has been successfully used in modeling the background statistics due to its capability to perform well without making any assumption about the form of the underlying distributions. However, in real-time applications, the O(N2) complexity of the method can be a bottleneck preventing the object tracking and event analysis modules from having the computing time needed. In this paper, we propose a new background subtraction technique, using multiresolution and recursive density estimation with mean shift based mode tracking. An algorithm with complexity independent on N is developed for fast, real-time implementation. Comparative results with known methods are included, in order to attest the effectiveness and quality of the proposed approach.

A Statistical Model for Wind Power Forecast Error Based on Kernel Density Estimation

2014 ◽  
Vol 8 (1) ◽  
pp. 501-507
Author(s):  
Liyang Liu ◽  
Junji Wu ◽  
Shaoliang Meng
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Forecast Error ◽  
Kernel Density ◽  
Wind Power Forecast ◽  
Proposed Model ◽  
Chi Squared

Wind power has been developed rapidly as a clean energy in recent years. The forecast error of wind power, however, makes it difficult to use wind power effectively. In some former statistical models, the forecast error was usually assumed to be a Gaussian distribution, which had proven to be unreliable after a statistical analysis. In this paper, a more suitable probability density function for wind power forecast error based on kernel density estimation was proposed. The proposed model is a non-parametric statistical algorithm and can directly obtain the probability density function from the error data, which do not need to make any assumptions. This paper also presented an optimal bandwidth algorithm for kernel density estimation by using particle swarm optimization, and employed a Chi-squared test to validate the model. Compared with Gaussian distribution and Beta distribution, the mean squared error and Chi-squared test show that the proposed model is more effective and reliable.

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