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Short-term wind power prediction for offshore wind farms Evaluation of Fuzzy-Neural network based models

Abstract : Future major developments of wind power capacities are likely to take place offshore. As for onshore wind parks, short-term wind power prediction up to 48 hours ahead is expected to be of major importance for the management of offshore farms and their secure integration to the grid. Modeling the behavior of large wind farms of several tens or hundreds of MWs installed capacity and covering areas of several square kilometers is going to be a challenge. The adaptation of wind power forecasting methods to reach the specificities of the offshore case is not straightforward and very few results are available in the literature. The paper presents the new considerations that have to be made when dealing with large offshore wind farms and therefore the necessary evolutions of prediction models. Then, a state-of-the-art fuzzy-neural network based wind power forecasting model is described. Its performance is assessed for offshore conditions and compared to its level of performance for typical onshore parks. A general methodology dedicated to large offshore wind farms is developed. In order to deal with the spread of the turbines in such cases, methods based on the division of large wind farms into clusters are proposed. Furthermore, the use of satellite images for mapping the wind flow behavior inside offshore parks is investigated.
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Pierre Pinson, Thierry Ranchin, Georges Kariniotakis. Short-term wind power prediction for offshore wind farms Evaluation of Fuzzy-Neural network based models. Global windpower Conference, Mar 2004, Chicago, United States. pp.CD ROM. ⟨hal-00529595⟩

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