Expectation-maximization algorithm for evaluation of wind direction characteristics

Directional statistical distributions can be used to model a wide range of industrial and phenomena. Finite mixtures of circular normal von Mises (MvM) distributions have been used to represent directional data from various domains including energy industry, medical science, and information retrieva...

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Bibliographic Details
Published in:	2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC) pp. 1730 - 1735
Main Authors:	Marek, Jaroslav, Heckenbergerova, Jana
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2015
Subjects:	Approximation algorithms Atmospheric modeling circular data Convergence Data models Expectation-Maximization algorithm Load modeling Mathematical model Maximum likelihood estimation Mixture of von Mises Distribution wind direction modelling
Online Access:	Get full text
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Summary:	Directional statistical distributions can be used to model a wide range of industrial and phenomena. Finite mixtures of circular normal von Mises (MvM) distributions have been used to represent directional data from various domains including energy industry, medical science, and information retrieval. This paper presents the probabilisticmodeling of the prevailing wind directions. Expectation-maximization algorithm (EM algorithm) is employed to evaluate unknown parameters of MvM distribution. The evaluation is carried out using real-world data sets describing annual wind direction at St. John's airport in Newfoundland, Canada. Experimental results show that EM algorithm is able to find good model parameters corresponding to input data. However, because the termination criterion χ 2 -function converges to 335, the resulting distribution cannot pass Pearson's test of goodness of fit.
DOI:	10.1109/EEEIC.2015.7165433