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Communication Dans Un Congrès Année : 2003

State-of-the-art Methods and software tools for short-term prediction of wind energy production

Résumé

The installed wind energy capacity in Europe today is 20 GW, while the projections for 2010 according to the Kyoto protocol and the EC directives is up to 40-60 GW. The large-scale integration of wind energy emerges the use of advanced operational tools for short-term forecasting of the wind production in the next hours up to the next 2-7 days. End-users (independent power producers, electric companies, transmission system operators, etc) recognize the contribution of wind prediction for a secure and economic operation of the power system. Especially, in a liberalized electricity market, prediction tools enhance the position of wind energy compared to other forms of dispatchable generation. The paper presents in detail the state-of the-art on the methods, the software tools and the relevant R&D projects for wind power forecasting. The paper finally presents experience by end-users that run operationally such prediction systems today as stand-alone applications or interfaced to EMS/DMS systems. The paper reviews the related literature on wind power prediction. Emphasis is given on operational tools such as WPPT, Prediktor, Zephyr, Previento, SIPREÓLICO, LocalPred, More-Care etc. The various models or tools are classified using criteria like: · The type of implemented approach i.e. timeseries (neural networks, ARMA etc) or physical. · The specific spatial scale focused by the models (regional, wind park scale, micro-scale). · The on-line performance of the prediction tools and their coupling to Energy Management Systems.
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Dates et versions

hal-00530022 , version 1 (27-10-2010)

Identifiants

  • HAL Id : hal-00530022 , version 1

Citer

Gregor Giebel, L. Landberg, Georges Kariniotakis, Richard Brownsword. State-of-the-art Methods and software tools for short-term prediction of wind energy production. EWEC 2003 (European Wind Energy Conference and exhibition), Jun 2003, Madrid, Spain. ⟨hal-00530022⟩
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