DocumentCode
2590905
Title
Next generation forecasting tools for the optimal management of wind generation
Author
Kariniotakis, G. ; Waldl, I.H.-P. ; Marti, I. ; Giebel, G. ; Nielsen, T.S. ; Tambke, J. ; Usaola, J. ; Dierich, F. ; Bocquet, A. ; Virlot, S.
Author_Institution
Centre for Energy & Processes, Ecole des Mines de Paris, Sophia Antipolis
fYear
2006
fDate
11-15 June 2006
Firstpage
1
Lastpage
6
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
Keywords
load forecasting; offshore installations; power engineering computing; power system management; risk management; statistical analysis; weather forecasting; wind power plants; ANEMOS project; maintenance scheduling; meteorological forecast; offshore wind farm; onshore wind farm; optimal management; prediction risk estimation; statistical modeling; wind generation; wind power forecasting; Power generation; Power system interconnection; Power system management; Power system reliability; Production; Weather forecasting; Wind energy; Wind energy generation; Wind forecasting; Wind power generation; Wind power; numerical weather predictions; on-line software; short-term forecasting; tools for wind integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Probabilistic Methods Applied to Power Systems, 2006. PMAPS 2006. International Conference on
Conference_Location
Stockholm
Print_ISBN
978-91-7178-585-5
Type
conf
DOI
10.1109/PMAPS.2006.360238
Filename
4202250
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