• 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