E3S Web Conf.
Volume 23, 2017World Renewable Energy Congress-17
|Number of page(s)||9|
|Section||9. Wind Energy|
|Published online||20 November 2017|
Online Bayesian Learning with Natural Sequential Prior Distribution Used for Wind Speed Prediction
Department of electronics, Faculty of Technology, Saad Dahlab University, Blida, Algeria
Laboratory of Electrical Systems and Remote control. P O Box 270 route de Soumaa, Blida 09000, Algeria
Predicting wind speed is one of the most important and critic tasks in a wind farm. All approaches, which directly describe the stochastic dynamics of the meteorological data are facing problems related to the nature of its non-Gaussian statistics and the presence of seasonal effects .In this paper, Online Bayesian learning has been successfully applied to online learning for three-layer perceptron’s used for wind speed prediction. First a conventional transition model based on the squared norm of the difference between the current parameter vector and the previous parameter vector has been used. We noticed that the transition model does not adequately consider the difference between the current and the previous wind speed measurement. To adequately consider this difference, we use a natural sequential prior. The proposed transition model uses a Fisher information matrix to consider the difference between the observation models more naturally. The obtained results showed a good agreement between both series, measured and predicted. The mean relative error over the whole data set is not exceeding 5 %.
Key words: artificial neural network / Bayesian learning / Fisher information / online learning / wind speed forecasting
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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