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Next generation forecasting tools for the optimal management of wind generation

Abstract : This paper presents the objectives and an overview of the results obtained in the frame of the ANEMOS project on short-term wind power forecasting. The aim of the project is to develop accurate models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting, exploiting both statistical and physical modeling approaches. The project focus on prediction horizons up to 48 hours ahead and investigates predictability of wind for higher horizons up to 7 days ahead useful i.e. for maintenance scheduling. Emphasis is given on the integration of high-resolution meteorological forecasts. Specific modules are also developed for on-line uncertainty and prediction risk estimation. An integrated software platform, 'ANEMOS', is developed to host the various models. This system is installed by several end-users for on-line operation at onshore and offshore wind farms for prediction at a local, regional and national scale. The applications include different terrain types and wind climates, on- and offshore cases, and interconnected or island grids.
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Submitted on : Thursday, October 14, 2010 - 3:59:13 PM
Last modification on : Friday, August 5, 2022 - 12:36:01 PM



Georges Kariniotakis, H.P. Waldl, Ignacio Marti, Gregor Giebel, T.S. Nielsen, et al.. Next generation forecasting tools for the optimal management of wind generation. 9th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2006, Jun 2006, Stockholm, Sweden. pp.1-6 - ISBN 978-91-7178-585-5, ⟨10.1109/PMAPS.2006.360238⟩. ⟨hal-00526448⟩



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