scholarly journals Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

2016 ◽  
Vol 5 (2) ◽  
pp. 220-227 ◽  
Author(s):  
Bo HU ◽  
Yudun LI ◽  
Hejun YANG ◽  
He WANG
Keyword(s):  
Wind Speed ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Reliability Assessment ◽  
Kernel Density ◽  
Model Based

Multivariate and Multimodal Wind Distribution Model Based on Kernel Density Estimation

2011 ◽  
Author(s):  
Jie Zhang ◽  
Souma Chowdhury ◽  
Achille Messac ◽  
Luciano Castillo
Keyword(s):  
Wind Speed ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Wind Direction ◽  
Wind Farm ◽  
Kernel Density ◽  
Distribution Model ◽  
Wind Condition ◽  
Air Density ◽  
Wind Distribution

This paper presents a new method to accurately characterize and predict the annual variation of wind conditions. Estimation of the distribution of wind conditions is necessary (i) to quantify the available energy (power density) at a site, and (ii) to design optimal wind farm configurations. We develop a smooth multivariate wind distribution model that captures the coupled variation of wind speed, wind direction, and air density. The wind distribution model developed in this paper also avoids the limiting assumption of unimodality of the distribution. This method, which we call the Multivariate and Multimodal Wind distribution (MMWD) model, is an evolution from existing wind distribution modeling techniques. Multivariate kernel density estimation, a standard non-parametric approach to estimate the probability density function of random variables, is adopted for this purpose. The MMWD technique is successfully applied to model (i) the distribution of wind speed (univariate); (ii) the distribution of wind speed and wind direction (bivariate); and (iii) the distribution of wind speed, wind direction, and air density (multivariate). The latter is a novel contribution of this paper, while the former offers opportunities for validation. Ten-year recorded wind data, obtained from the North Dakota Agricultural Weather Network (NDAWN), is used in this paper. We found the coupled distribution to be multimodal. A strong correlation among the wind condition parameters was also observed.

Wind speed forecasting based on Quantile Regression Minimal Gated Memory Network and Kernel Density Estimation

2019 ◽  
Vol 196 ◽  
pp. 1395-1409 ◽  
Author(s):  
Zhendong Zhang ◽  
Hui Qin ◽  
Yongqi Liu ◽  
Liqiang Yao ◽  
Xiang Yu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Wind Speed Forecasting ◽  
Memory Network
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