A novel probabilistic wind speed prediction approach using real time refined variational model decomposition and conditional kernel density estimation

A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS) algorithm and artificial neural network (ANN) method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE) and root mean square error (RMSE) are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

Download Full-text

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

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee0501127i ◽

2005 ◽

Vol 18 (1) ◽

pp. 127-144 ◽

Cited By ~ 14

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.

Download Full-text

Multivariate and Multimodal Wind Distribution Model Based on Kernel Density Estimation

ASME 2011 5th International Conference on Energy Sustainability, Parts A, B, and C ◽

10.1115/es2011-54507 ◽

2011 ◽

Cited By ~ 10

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.

Download Full-text