Probabilistic Ramp Detection and Forecasting for Wind Power Prediction

  • C. Ferreira
  • J. Gama
  • V. Miranda
  • A. Botterud
Part of the Reliable and Sustainable Electric Power and Energy Systems Management book series (RSEPESM)


This chapter proposes a new way to detect and represent the probability of ramping events in short-term wind power forecasting. Ramping is one notable characteristic in a time series associated with a drastic change in value in a set of consecutive time steps. Two properties of a ramp event forecast, that is, slope and phase error, are important from the point of view of the system operator (SO): they have important implications in the decisions associated with unit commitment or generation scheduling, especially if there is thermal generation dominance in the power system. Unit commitment decisions, generally taken some 12–48 h in advance, must prepare the generation schedule in order to smoothly accommodate forecasted drastic changes in wind power availability.


Receiver Operating Characteristic Curve Wind Power Wind Farm Forecast Horizon Unit Commitment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (‘Argonne’). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up non-exclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The authors acknowledge EDP Renewables North America for providing the wind farm data used in the analysis.

The general fundamental work at INESC TEC is partially funded by the ERDF from the EU through the Programme COMPETE and by the Portuguese Government through FCT—Foundation for Science and Technology, namely under PEST-C/EEI/LA 0014/2011 and project ref. LASCA PTDC/EEA-EEL/104278/2008 and GEMS PTDC/EEA-EEL/105261/2008.


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Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.LIAAD/INESC TEC and ISEP/IPPPolytechnic Institute of PortoPortoPortugal
  2. 2.LIAAD/INESC TEC and FEPUniversity of PortoPortoPortugal
  3. 3.INESC TEC (Formerly INESC Porto), and FEUPUniversity of PortoPortoPortugal
  4. 4.Argonne National LaboratoryDecision and Information Sciences DivisionArgonneUSA

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