Abstract
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 %.
Highlights
Due to the high penetration of wind power in the electricity system, the forecast accuracy of wind power prediction systems becomes increasingly important
The results show that even for the same wind dataset, no single neural network model outperforms others universally in terms of all evaluation metrics
The three-layer perceptron defined as follows is used as the function f (·)
Summary
Due to the high penetration of wind power in the electricity system, the forecast accuracy of wind power prediction systems becomes increasingly important. Many scholars have done a lot of research on wind power prediction. The forecast accuracy has improved constantly, and it can be expected that intense research and development efforts are already on track. Many researches were achieved in order to predict wind behaviour. It is still one of the most difficult quantities to forecast [1], namely due its stochastic nature. The actual state of the art includes five main families of methods: persistence Method [2], physical Methods[3], spatial Correlation Models[5], artificial Intelligence Methods[6] and hybrid Methods[7].there will always be an inherent and irreducible uncertainty in every prediction
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