CFAN, Climate Forecast Applications Network, LLC.

                                                       © 2019.  All Rights Reserved 

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 Telephone:  USA  +1.404.803.2012

 Email:  info@cfanclimate.com 

Wind Power

 

 

 

 

 

 

 

 

 

 

CFAN’s offerings in wind power prediction focus on time horizons beyond the conventional 48-72 hour window that is a primary time period for many market solutions currently in existence.  Our current suite of forecasts includes:

  • 1-15 day probabilistic forecasts of zonal wind / wind power for key wind generation regions in the U.S.

  • 16-32 day probabilistic forecasts of zonal wind / wind power along with supporting large scale influential weather regime outlooks (twice weekly)

  • 6 month probabilistic forecasts of regional wind power for the US and Europe (monthly)

 

 

Recognizing the importance of wind ramps, we have implemented operational prediction of large-scale ramp events. Wind anomalies are calculated also removing systematic features (such as the diurnal cycle) and other biases (such as model drift), eliminating the need for other statistical adjustments to the power time series.  Ramp up and down events are highlighted and marked with probability information.  This information is forecast at point locations allowing for the end-user to also follow the propagation behavior across a spatial domain.

The need for wind power forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. 

CFAN provides wind speed forecasts paired with derived wind power % of capacity. Pairing CFAN’s medium and extended range wind forecasts with weather regime (teleconnection) information provides basis understanding of pronounced deviations from normal.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Matching wind anomaly forecasts with population weighted temperature anomalies gives insight where power demand and supply issues might be most critical.