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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Improved Wind Power Forecasting Using a Combined Neuro-fuzzy and Artificial Neural Network Model
verfasst von : Yiannis A. Katsigiannis, Antonis G. Tsikalakis, Pavlos S. Georgilakis, Nikos D. Hatziargyriou
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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The intermittent nature of the wind creates significant uncertainty in the operation of power systems with increased wind power penetration. Con- siderable efforts have been made for the accurate prediction of the wind power using either statistical or physical models. In this paper, a method based on Artificial Neural Network (ANN) is proposed in order to improve the predictions of an existing neuro-fuzzy wind power forecasting model taking into account the evaluation results from the use of this wind power forecasting tool. Thus, an improved wind power forecasting is achieved and a better estimation of the confidence interval of the proposed model is provided.